"""This module contains some common functions for both folium and ipyleaflet."""

import csv
import json
import os
import shutil
import subprocess
import sys
import tarfile
import urllib.request
import warnings
import zipfile
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, Iterator

import folium
import ipyleaflet
import ipywidgets as widgets
import numpy as np
import pandas as pd
import requests
import whitebox
import xyzservices

from .stac import *

try:
    from IPython.display import display
except ImportError:
    pass

if TYPE_CHECKING:
    import geopandas as gpd
    from obstore.auth.boto3 import Boto3CredentialProvider


class WhiteboxTools(whitebox.WhiteboxTools):
    """This class inherits the whitebox WhiteboxTools class."""

    def __init__(self, **kwargs: Any):
        super().__init__(**kwargs)


def whiteboxgui(
    verbose: Optional[bool] = True,
    tree: Optional[bool] = False,
    reset: Optional[bool] = False,
    sandbox_path: Optional[str] = None,
) -> dict:
    """Shows the WhiteboxTools GUI.

    Args:
        verbose (bool, optional): Whether to show progress info when the tool is running. Defaults to True.
        tree (bool, optional): Whether to use the tree mode toolbox built using ipytree rather than ipywidgets. Defaults to False.
        reset (bool, optional): Whether to regenerate the json file with the dictionary containing the information for all tools. Defaults to False.
        sandbox_path (str, optional): The path to the sandbox folder. Defaults to None.

    Returns:
        A toolbox GUI.
    """
    import whiteboxgui

    return whiteboxgui.show(verbose, tree, reset, sandbox_path)


def _in_colab_shell() -> bool:
    """Tests if the code is being executed within Google Colab."""
    import sys

    if "google.colab" in sys.modules:
        return True
    else:
        return False


def _is_drive_mounted() -> bool:
    """Checks whether Google Drive is mounted in Google Colab.

    Returns:
        True if Google Drive is mounted, False otherwise.
    """
    drive_path = "/content/drive/My Drive"
    if os.path.exists(drive_path):
        return True
    else:
        return False


def _get_jupyterhub_client_params(
    host: str = None, port: int = None, prefix: str = None
) -> Tuple[str, int, str]:
    """Get client parameters for JupyterHub/remote Jupyter environments.

    This function reads environment variables to configure how tile server URLs
    should be constructed when running in remote Jupyter environments like JupyterHub.
    This is necessary because the browser needs to access the tile server through
    a proxy URL rather than directly via localhost.

    Environment variables (similar to localtileserver):
        LEAFMAP_CLIENT_HOST: The host that the client browser should use to access the server
        LEAFMAP_CLIENT_PORT: The port that the client browser should use to access the server
        LEAFMAP_CLIENT_PREFIX: The URL prefix to use, e.g., 'proxy/{port}' or
                              '/user/{username}/proxy/{port}' for JupyterHub

    Args:
        host (str, optional): Override host. If None, uses environment variable.
        port (int, optional): Override port. If None, uses environment variable.
        prefix (str, optional): Override URL prefix. If None, uses environment variable.

    Returns:
        Tuple[str, int, str]: (host, port, prefix) for client-side URL construction

    Example:
        In a JupyterHub environment, set this in your notebook:
        >>> import os
        >>> os.environ['LEAFMAP_CLIENT_PREFIX'] = f"{os.environ['JUPYTERHUB_SERVICE_PREFIX']}/proxy/{{port}}"
    """
    if (
        host is None
        and "LEAFMAP_CLIENT_HOST" in os.environ
        and os.environ["LEAFMAP_CLIENT_HOST"]
    ):
        host = str(os.environ["LEAFMAP_CLIENT_HOST"])

    if (
        port is None
        and "LEAFMAP_CLIENT_PORT" in os.environ
        and os.environ["LEAFMAP_CLIENT_PORT"]
    ):
        port = int(os.environ["LEAFMAP_CLIENT_PORT"])

    if (
        prefix is None
        and "LEAFMAP_CLIENT_PREFIX" in os.environ
        and os.environ["LEAFMAP_CLIENT_PREFIX"]
    ):
        prefix = str(os.environ["LEAFMAP_CLIENT_PREFIX"])

    return host, port, prefix


def _get_browser_origin():
    """Try to get the browser's origin (protocol + host) using JavaScript.

    Returns:
        str: The browser origin (e.g., "https://example.com") or None if not available
    """
    try:
        from IPython.display import Javascript, display
        from IPython import get_ipython

        # Try to execute JavaScript to get window.location.origin
        # This only works in Jupyter environments
        ipython = get_ipython()
        if ipython is not None:
            # Use eval_js if available (Jupyter Notebook/Lab)
            if hasattr(ipython, "kernel"):
                try:
                    # Note: This doesn't actually work reliably, but we try
                    result = ipython.kernel.do_one_iteration()
                    return None  # JavaScript evaluation doesn't work from Python side
                except:
                    pass
    except:
        pass
    return None


def configure_jupyterhub(base_url: str = None):
    """Auto-configure leafmap for JupyterHub environments.

    This function automatically detects if running in a JupyterHub environment
    and configures the necessary environment variables for tile server proxying.
    It will be called automatically by add_duckdb_layer, so you typically don't
    need to call it manually.

    Args:
        base_url (str, optional): The base URL of your JupyterHub instance
            (e.g., "https://jupyter.example.com"). Required for MapLibre vector tiles
            due to URL validation requirements. If not provided, vector tiles may not work.

    Example:
        >>> import leafmap
        >>> # Specify your JupyterHub URL for vector tile support
        >>> leafmap.configure_jupyterhub("https://your-jupyterhub-domain.com")
        >>> # Now add_duckdb_layer will work correctly

    Note:
        MapLibre GL JS's vector tile sources require absolute URLs (with http:// or https://).
        Raster tiles (add_raster) work with relative URLs, but vector tiles (add_duckdb_layer) do not.
        This is a limitation of MapLibre GL JS, not leafmap.
    """
    # Store base URL if provided
    if base_url:
        base_url = base_url.rstrip("/")
        os.environ["LEAFMAP_BASE_URL"] = base_url

    # Auto-configure proxy prefix if not already set (like get_local_tile_url does)
    if "LEAFMAP_CLIENT_PREFIX" not in os.environ:
        # Check for JupyterHub environment
        if "JUPYTERHUB_SERVICE_PREFIX" in os.environ:
            jupyterhub_prefix = os.environ["JUPYTERHUB_SERVICE_PREFIX"].rstrip("/")
            os.environ["LEAFMAP_CLIENT_PREFIX"] = f"{jupyterhub_prefix}/proxy/{{port}}"
        elif "JUPYTER_SERVER_ROOT" in os.environ or "JPY_SESSION_NAME" in os.environ:
            # Generic Jupyter environment
            os.environ["LEAFMAP_CLIENT_PREFIX"] = "proxy/{port}"


def set_proxy(
    port: Optional[int] = 1080, ip: Optional[str] = "http://127.0.0.1"
) -> None:
    """Sets proxy if needed. This is only needed for countries where Google services are not available.

    Args:
        port (int, optional): The proxy port number. Defaults to 1080.
        ip (str, optional): The IP address. Defaults to 'http://127.0.0.1'.
    """

    if not ip.startswith("http://") and not ip.startswith("https://"):
        ip = f"http://{ip}"
    proxy = f"{ip}:{port}"

    os.environ["HTTP_PROXY"] = proxy
    os.environ["HTTPS_PROXY"] = proxy

    try:
        response = requests.get("https://google.com")
        response.raise_for_status()
    except requests.exceptions.RequestException as e:
        print(
            "Failed to connect to Google Services. "
            "Please double check the port number and IP address."
        )
        print(f"Error: {e}")


def _check_install(package: str) -> None:
    """Checks whether a package is installed. If not, it will install the package.

    Args:
        package (str): The name of the package to check.
    """
    import subprocess

    try:
        __import__(package)
        # print('{} is already installed.'.format(package))
    except ImportError:
        print(f"{package} is not installed. Installing...")
        try:
            subprocess.check_call(["python", "-m", "pip", "install", package])
        except subprocess.CalledProcessError as e:
            print(f"Failed to install {package}, Error: {e}")
            raise
        print(f"{package} has been installed successfully.")


def update_package() -> None:
    """Updates the leafmap package from the leafmap GitHub repository without the need to use pip or conda.
    In this way, I don't have to keep updating pypi and conda-forge with every minor update of the package.

    """

    download_dir = Path.home() / "Downloads"
    download_dir.mkdir(parents=True, exist_ok=True)
    _clone_repo(out_dir=str(download_dir))
    pkg_dir = download_dir / "leafmap-master"
    work_dir = Path.cwd()

    os.chdir(pkg_dir)
    try:
        if shutil.which("pip"):
            cmd = ["pip", "install", "."]
        else:
            cmd = ["pip3", "install", "."]
        subprocess.run(cmd, check=True)
    except subprocess.CalledProcessError as error:
        print(f"Failed to install the package: {error}")
    finally:
        os.chdir(work_dir)

    print(
        "\nPlease comment out 'leafmap.update_package()' and restart kernel to take effect:\n"
        "Jupyter menu -> Kernel -> Restart & Clear Output"
    )


def check_package(name: str, URL: Optional[str] = "") -> None:
    try:
        __import__(name.lower())
    except Exception:
        raise ImportError(
            f"{name} is not installed. Please install it before proceeding. {URL}"
        )


def _clone_repo(out_dir: Optional[str] = ".", unzip: Optional[bool] = True) -> None:
    """Clones the leafmap GitHub repository.

    Args:
        out_dir (str, optional): Output folder for the repo. Defaults to '.'.
        unzip (bool, optional): Whether to unzip the repository. Defaults to True.
    """
    url = "https://github.com/opengeos/leafmap/archive/master.zip"
    filename = "leafmap-master.zip"

    try:
        if not isinstance(out_dir, str):
            raise ValueError(
                f"The out_dir parametermust be string. Provided: {type(out_dir).__name__}"
            )
        download_from_url(url, out_file_name=filename, out_dir=out_dir, unzip=unzip)
    except Exception as error:
        print(f"An error occurred while clone the repository: {str(error)}")


def __install_from_github(url: str) -> None:
    """Install a package from a GitHub repository.

    Args:
        url (str): The URL of the GitHub repository.
    """

    try:
        download_dir = os.path.join(os.path.expanduser("~"), "Downloads")
        if not os.path.exists(download_dir):
            os.makedirs(download_dir)

        repo_name = os.path.basename(url)
        zip_url = os.path.join(url, "archive/master.zip")
        filename = repo_name + "-master.zip"
        download_from_url(
            url=zip_url, out_file_name=filename, out_dir=download_dir, unzip=True
        )

        pkg_dir = os.path.join(download_dir, repo_name + "-master")
        pkg_name = os.path.basename(url)
        work_dir = os.getcwd()
        os.chdir(pkg_dir)
        print("Installing {}...".format(pkg_name))
        cmd = "pip install ."
        os.system(cmd)
        os.chdir(work_dir)
        print("{} has been installed successfully.".format(pkg_name))
        # print("\nPlease comment out 'install_from_github()' and restart the kernel to take effect:\nJupyter menu -> Kernel -> Restart & Clear Output")

    except Exception as e:
        raise Exception(e)


def _check_git_install() -> bool:
    """Checks if Git is installed.

    Returns:
        True if Git is installed, otherwise False.
    """
    import webbrowser

    cmd = "git --version"
    output = os.popen(cmd).read()

    if "git version" in output:
        return True
    else:
        url = "https://git-scm.com/downloads"
        print(
            "Git is not installed. Please download Git from {} and install it.".format(
                url
            )
        )
        webbrowser.open_new_tab(url)
        return False


def _clone_github_repo(url: str, out_dir: str) -> None:
    """Clones a GitHub repository.

    Args:
        url (str): The link to the GitHub repository
        out_dir (str): The output directory for the cloned repository.
    """

    repo_name = os.path.basename(url)
    # url_zip = os.path.join(url, 'archive/master.zip')
    url_zip = url + "/archive/master.zip"

    if os.path.exists(out_dir):
        print(
            "The specified output directory already exists. Please choose a new directory."
        )
        return

    parent_dir = os.path.dirname(out_dir)
    out_file_path = os.path.join(parent_dir, repo_name + ".zip")

    try:
        urllib.request.urlretrieve(url_zip, out_file_path)
    except Exception:
        print("The provided URL is invalid. Please double check the URL.")
        return

    with zipfile.ZipFile(out_file_path, "r") as zip_ref:
        zip_ref.extractall(parent_dir)

    src = out_file_path.replace(".zip", "-master")
    os.rename(src, out_dir)
    os.remove(out_file_path)


def _is_tool(name: str) -> Optional[bool]:
    """Check whether `name` is on PATH and marked as executable."""
    try:
        return shutil.which(name) is not None
    except FileNotFoundError:
        return None


def random_string(string_length: Optional[int] = 3) -> str:
    """Generates a random string of fixed length.

    Args:
        string_length (int, optional): Fixed length. Defaults to 3.

    Returns:
        A random string.
    """
    import random
    import string

    # random.seed(1001)
    letters = string.ascii_lowercase
    return "".join(random.choice(letters) for i in range(string_length))


def open_image_from_url(url: str) -> Any:
    """Loads an image from the specified URL.

    Args:
        url (str): URL of the image.

    Returns:
        An image object.
    """
    from io import BytesIO

    from PIL import Image

    # from urllib.parse import urlparse

    try:
        response = requests.get(url)
        img = Image.open(BytesIO(response.content))
        return img
    except Exception as e:
        print(e)


def show_image(
    img_path: str, width: Optional[int] = None, height: Optional[int] = None
):
    """Shows an image within Jupyter notebook.

    Args:
        img_path (str): The image file path.
        width (int, optional): Width of the image in pixels. Defaults to None.
        height (int, optional): Height of the image in pixels. Defaults to None.

    """
    from IPython.display import display

    try:
        out = widgets.Output()
        # layout={'border': '1px solid black'})
        # layout={'border': '1px solid black', 'width': str(width + 20) + 'px', 'height': str(height + 10) + 'px'},)
        out.clear_output()
        out.outputs = ()

        display(out)
        with out:
            file = open(img_path, "rb")
            image = file.read()
            if (width is None) and (height is None):
                out.append_display_data(widgets.Image(value=image))
            elif (width is not None) and (height is not None):
                out.append_display_data(
                    widgets.Image(value=image, width=width, height=height)
                )
            else:
                print("You need set both width and height.")
                return
    except Exception as e:
        print(e)


def show_html(html: str) -> None:
    """Shows HTML within Jupyter notebook.

    Args:
        html (str): File path or HTML string.

    Raises:
        FileNotFoundError: If the file does not exist.

    Returns:
        An ipywidgets.HTML widget.
    """
    if os.path.exists(html):
        with open(html, "r") as f:
            content = f.read()

        widget = widgets.HTML(value=content)
        return widget
    else:
        try:
            widget = widgets.HTML(value=html)
            return widget
        except Exception as e:
            raise Exception(e)


def display_html(
    html: Union[str, bytes], width: str = "100%", height: int = 500
) -> None:
    """
    Displays an HTML file or HTML string in a Jupyter Notebook.

    Args:
        html (Union[str, bytes]): Path to an HTML file or an HTML string.
        width (str, optional): Width of the displayed iframe. Default is '100%'.
        height (int, optional): Height of the displayed iframe. Default is 500.

    Returns:
        None
    """
    from IPython.display import IFrame, display

    if isinstance(html, str) and html.startswith("<"):
        # If the input is an HTML string
        html_content = html
    elif isinstance(html, str):
        # If the input is a file path
        with open(html, "r") as file:
            html_content = file.read()
    elif isinstance(html, bytes):
        # If the input is a byte string
        html_content = html.decode("utf-8")
    else:
        raise ValueError("Invalid input type. Expected a file path or an HTML string.")

    display(IFrame(src=html_content, width=width, height=height))


def has_transparency(img) -> bool:
    """Checks whether an image has transparency.

    Args:
        img (object):  a PIL Image object.

    Returns:
        True if it has transparency, False otherwise.
    """

    if img.mode == "P":
        transparent = img.info.get("transparency", -1)
        for _, index in img.getcolors():
            if index == transparent:
                return True
    elif img.mode == "RGBA":
        extrema = img.getextrema()
        if extrema[3][0] < 255:
            return True

    return False


def upload_to_imgur(in_gif: str) -> str:
    """Uploads an image to imgur.com

    Args:
        in_gif (str): The file path to the image.
    """
    import subprocess

    pkg_name = "imgur-uploader"
    if not _is_tool(pkg_name):
        _check_install(pkg_name)

    try:
        IMGUR_API_ID = os.environ.get("IMGUR_API_ID", None)
        IMGUR_API_SECRET = os.environ.get("IMGUR_API_SECRET", None)
        credentials_path = os.path.join(
            os.path.expanduser("~"), ".config/imgur_uploader/uploader.cfg"
        )

        if (
            (IMGUR_API_ID is not None) and (IMGUR_API_SECRET is not None)
        ) or os.path.exists(credentials_path):
            proc = subprocess.Popen(["imgur-uploader", in_gif], stdout=subprocess.PIPE)
            for _ in range(0, 2):
                line = proc.stdout.readline()
                print(line.rstrip().decode("utf-8"))
            # while True:
            #     line = proc.stdout.readline()
            #     if not line:
            #         break
            #     print(line.rstrip().decode("utf-8"))
        else:
            print(
                "Imgur API credentials could not be found. Please check https://pypi.org/project/imgur-uploader/ for instructions on how to get Imgur API credentials"
            )
            return

    except Exception as e:
        raise Exception(e)


def rgb_to_hex(rgb: Optional[Tuple[int, int, int]] = (255, 255, 255)) -> str:
    """Converts RGB to hex color. In RGB color R stands for Red, G stands for Green, and B stands for Blue, and it ranges from the decimal value of 0 – 255.

    Args:
        rgb (tuple, optional): RGB color code as a tuple of (red, green, blue). Defaults to (255, 255, 255).

    Returns:
        A hex color code.
    """
    return "%02x%02x%02x" % rgb


def hex_to_rgb(value: Optional[str] = "FFFFFF") -> Tuple[int, int, int]:
    """Converts hex color to RGB color.

    Args:
        value (str, optional): Hex color code as a string. Defaults to 'FFFFFF'.

    Returns:
        RGB color as a tuple.
    """
    value = value.lstrip("#")
    lv = len(value)
    return tuple(int(value[i : i + lv // 3], 16) for i in range(0, lv, lv // 3))


def check_color(in_color: Union[str, Tuple, List]) -> str:
    """Checks the input color and returns the corresponding hex color code.

    Args:
        in_color (str or tuple or list): It can be a string (e.g., 'red', '#ffff00', 'ffff00', 'ff0') or RGB tuple/list (e.g., (255, 127, 0)).

    Returns:
        A hex color code.
    """
    from matplotlib import colors

    out_color = "#000000"  # default black color
    # Handle RGB tuple or list
    if isinstance(in_color, (tuple, list)) and len(in_color) == 3:
        # rescale color if necessary
        if all(isinstance(item, int) for item in in_color):
            # Ensure values are floats between 0 and 1 for to_hex
            in_color = [c / 255.0 for c in in_color]
        try:
            return colors.to_hex(in_color)
        except ValueError:
            print(
                f"The provided RGB color ({in_color}) is invalid. Using the default black color."
            )
            return out_color

    # Handle string color input
    elif isinstance(in_color, str):
        if in_color.startswith("rgba"):
            return in_color
        try:
            # Try converting directly (handles color names and hex with #)
            return colors.to_hex(in_color)
        except ValueError:
            try:
                # Try again by adding an extra # (handles hex without #)
                return colors.to_hex(f"#{in_color}")
            except ValueError:
                print(
                    f"The provided color string ({in_color}) is invalid. Using the default black color."
                )
                return out_color
    else:
        print(
            f"The provided color type ({type(in_color)}) is invalid. Using the default black color."
        )
        return out_color


def system_fonts(show_full_path: Optional[bool] = False) -> List:
    """Gets a list of system fonts

        # Common font locations:
        # Linux: /usr/share/fonts/TTF/
        # Windows: C:/Windows/Fonts
        # macOS:  System > Library > Fonts

    Args:
        show_full_path (bool, optional): Whether to show the full path of each system font. Defaults to False.

    Returns:
        A list of system fonts.
    """
    try:
        import matplotlib.font_manager

        font_list = matplotlib.font_manager.findSystemFonts(
            fontpaths=None, fontext="ttf"
        )
        font_list.sort()

        font_names = [os.path.basename(f) for f in font_list]
        font_names.sort()

        if show_full_path:
            return font_list
        else:
            return font_names

    except Exception as e:
        raise Exception(e)


def download_from_url(
    url: str,
    out_file_name: Optional[str] = None,
    out_dir: Optional[str] = ".",
    unzip: Optional[bool] = True,
    verbose: Optional[bool] = True,
):
    """Download a file from a URL (e.g., https://github.com/opengeos/whitebox-python/raw/master/examples/testdata.zip)

    Args:
        url (str): The HTTP URL to download.
        out_file_name (str, optional): The output file name to use. Defaults to None.
        out_dir (str, optional): The output directory to use. Defaults to '.'.
        unzip (bool, optional): Whether to unzip the downloaded file if it is a zip file. Defaults to True.
        verbose (bool, optional): Whether to display or not the output of the function
    """
    in_file_name = os.path.basename(url)
    out_dir = check_dir(out_dir)

    if out_file_name is None:
        out_file_name = in_file_name
    out_file_path = os.path.join(out_dir, out_file_name)

    if verbose:
        print("Downloading {} ...".format(url))

    try:
        urllib.request.urlretrieve(url, out_file_path)
    except Exception:
        raise Exception("The URL is invalid. Please double check the URL.")

    final_path = out_file_path

    if unzip:
        # if it is a zip file
        if ".zip" in out_file_name:
            if verbose:
                print("Unzipping {} ...".format(out_file_name))
            with zipfile.ZipFile(out_file_path, "r") as zip_ref:
                zip_ref.extractall(out_dir)
            final_path = os.path.join(
                os.path.abspath(out_dir), out_file_name.replace(".zip", "")
            )

        # if it is a tar file
        if ".tar" in out_file_name:
            if verbose:
                print("Unzipping {} ...".format(out_file_name))
            with tarfile.open(out_file_path, "r") as tar_ref:
                with tarfile.open(out_file_path, "r") as tar_ref:

                    def is_within_directory(directory, target):
                        abs_directory = os.path.abspath(directory)
                        abs_target = os.path.abspath(target)

                        prefix = os.path.commonprefix([abs_directory, abs_target])

                        return prefix == abs_directory

                    def safe_extract(
                        tar, path=".", members=None, *, numeric_owner=False
                    ):
                        for member in tar.getmembers():
                            member_path = os.path.join(path, member.name)
                            if not is_within_directory(path, member_path):
                                raise Exception("Attempted Path Traversal in Tar File")

                        tar.extractall(path, members, numeric_owner=numeric_owner)

                    safe_extract(tar_ref, out_dir)

            final_path = os.path.join(
                os.path.abspath(out_dir), out_file_name.replace(".tart", "")
            )

    if verbose:
        print("Data downloaded to: {}".format(final_path))


def create_download_link(
    filename: str,
    title: str = "Click here to download: ",
    basename: Optional[str] = None,
) -> Any:
    """Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578

    Args:
        filename (str): The file path to the file to download
        title (str, optional): str. Defaults to "Click here to download: ".

    Returns:
        HTML download URL.
    """
    import base64

    from IPython.display import HTML

    data = open(filename, "rb").read()
    b64 = base64.b64encode(data)
    payload = b64.decode()
    if basename is None:
        basename = os.path.basename(filename)
    html = '<a download="{filename}" href="data:text/csv;base64,{payload}" style="color:#0000FF;" target="_blank">{title}</a>'
    html = html.format(payload=payload, title=title + f" {basename}", filename=basename)
    return HTML(html)


def edit_download_html(
    htmlWidget: Any, filename: str, title: str = "Click here to download: "
) -> None:
    """Downloads a file from voila. Adopted from https://github.com/voila-dashboards/voila/issues/578#issuecomment-617668058

    Args:
        htmlWidget (object): The HTML widget to display the URL.
        filename (str): File path to download.
        title (str, optional): Download description. Defaults to "Click here to download: ".
    """

    # from IPython.display import HTML
    # import ipywidgets as widgets
    import base64

    # Change widget html temporarily to a font-awesome spinner
    htmlWidget.value = '<i class="fa fa-spinner fa-spin fa-2x fa-fw"></i><span class="sr-only">Loading...</span>'

    # Process raw data
    data = open(filename, "rb").read()
    b64 = base64.b64encode(data)
    payload = b64.decode()

    basename = os.path.basename(filename)

    # Create and assign html to widget
    html = '<a download="{filename}" href="data:text/csv;base64,{payload}" target="_blank">{title}</a>'
    htmlWidget.value = html.format(
        payload=payload, title=title + basename, filename=basename
    )


def csv_points_to_shp(
    in_csv: str, out_shp: str, latitude: str = "latitude", longitude: str = "longitude"
) -> None:
    """Converts a csv file containing points (latitude, longitude) into a shapefile.

    Args:
        in_csv (str): File path or HTTP URL to the input csv file. For example, https://raw.githubusercontent.com/opengeos/data/main/world/world_cities.csv
        out_shp (str): File path to the output shapefile.
        latitude (str, optional): Column name for the latitude column. Defaults to 'latitude'.
        longitude (str, optional): Column name for the longitude column. Defaults to 'longitude'.

    """

    if in_csv.startswith("http") and in_csv.endswith(".csv"):
        out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
        out_name = os.path.basename(in_csv)

        if not os.path.exists(out_dir):
            os.makedirs(out_dir)
        download_from_url(in_csv, out_dir=out_dir)
        in_csv = os.path.join(out_dir, out_name)

    wbt = whitebox.WhiteboxTools()
    in_csv = os.path.abspath(in_csv)
    out_shp = os.path.abspath(out_shp)

    if not os.path.exists(in_csv):
        raise Exception("The provided csv file does not exist.")

    with open(in_csv, encoding="utf-8") as csv_file:
        reader = csv.DictReader(csv_file)
        fields = reader.fieldnames
        xfield = fields.index(longitude)
        yfield = fields.index(latitude)

    wbt.csv_points_to_vector(in_csv, out_shp, xfield=xfield, yfield=yfield, epsg=4326)


def csv_to_shp(
    in_csv, out_shp, latitude="latitude", longitude="longitude", encoding="utf-8"
):
    """Converts a csv file with latlon info to a point shapefile.

    Args:
        in_csv (str): The input csv file containing longitude and latitude columns.
        out_shp (str): The file path to the output shapefile.
        latitude (str, optional): The column name of the latitude column. Defaults to 'latitude'.
        longitude (str, optional): The column name of the longitude column. Defaults to 'longitude'.
    """
    try:
        import shapefile as shp
    except ImportError:
        install_package("pyshp")
        import shapefile as shp

    if in_csv.startswith("http") and in_csv.endswith(".csv"):
        in_csv = github_raw_url(in_csv)
        in_csv = download_file(in_csv, quiet=True, overwrite=True)

    try:
        points = shp.Writer(out_shp, shapeType=shp.POINT)
        with open(in_csv, encoding=encoding) as csvfile:
            csvreader = csv.DictReader(csvfile)
            header = csvreader.fieldnames
            [points.field(field) for field in header]
            for row in csvreader:
                points.point((float(row[longitude])), (float(row[latitude])))
                points.record(*tuple([row[f] for f in header]))

        out_prj = out_shp.replace(".shp", ".prj")
        with open(out_prj, "w") as f:
            prj_str = 'GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.0174532925199433]] '
            f.write(prj_str)

    except Exception as e:
        raise Exception(e)


def df_to_geojson(
    df,
    out_geojson=None,
    latitude="latitude",
    longitude="longitude",
    encoding="utf-8",
):
    """Creates points for a Pandas DataFrame and exports data as a GeoJSON.

    Args:
        df (pandas.DataFrame): The input Pandas DataFrame.
        out_geojson (str): The file path to the exported GeoJSON. Default to None.
        latitude (str, optional): The name of the column containing latitude coordinates. Defaults to "latitude".
        longitude (str, optional): The name of the column containing longitude coordinates. Defaults to "longitude".
        encoding (str, optional): The encoding of characters. Defaults to "utf-8".

    """

    import json

    from geojson import Feature, FeatureCollection, Point

    if out_geojson is not None:
        out_dir = os.path.dirname(os.path.abspath(out_geojson))
        if not os.path.exists(out_dir):
            os.makedirs(out_dir)

    features = df.apply(
        lambda row: Feature(
            geometry=Point((float(row[longitude]), float(row[latitude]))),
            properties=dict(row),
        ),
        axis=1,
    ).tolist()

    geojson = FeatureCollection(features=features)

    if out_geojson is None:
        return geojson
    else:
        with open(out_geojson, "w", encoding=encoding) as f:
            f.write(json.dumps(geojson))


def csv_to_geojson(
    in_csv: str,
    out_geojson: Optional[str] = None,
    latitude: str = "latitude",
    longitude: str = "longitude",
    encoding: str = "utf-8",
) -> Optional[Dict[str, Any]]:
    """Creates points for a CSV file and exports data as a GeoJSON.

    Args:
        in_csv (str): The file path to the input CSV file.
        out_geojson (str): The file path to the exported GeoJSON. Default to None.
        latitude (str, optional): The name of the column containing latitude coordinates. Defaults to "latitude".
        longitude (str, optional): The name of the column containing longitude coordinates. Defaults to "longitude".
        encoding (str, optional): The encoding of characters. Defaults to "utf-8".

    """

    import pandas as pd

    in_csv = github_raw_url(in_csv)

    if out_geojson is not None:
        out_geojson = check_file_path(out_geojson)

    df = pd.read_csv(in_csv)
    geojson = df_to_geojson(
        df, latitude=latitude, longitude=longitude, encoding=encoding
    )

    if out_geojson is None:
        return geojson
    else:
        with open(out_geojson, "w", encoding=encoding) as f:
            f.write(json.dumps(geojson))


def csv_to_gdf(
    in_csv,
    latitude="latitude",
    longitude="longitude",
    geometry=None,
    crs="EPSG:4326",
    encoding="utf-8",
    **kwargs: Any,
) -> "gpd.GeoDataFrame":
    """Creates points for a CSV file and converts them to a GeoDataFrame.

    Args:
        in_csv (str): The file path to the input CSV file.
        latitude (str, optional): The name of the column containing latitude coordinates. Defaults to "latitude".
        longitude (str, optional): The name of the column containing longitude coordinates. Defaults to "longitude".
        geometry (str, optional): The name of the column containing geometry. Defaults to None.
        crs (str, optional): The coordinate reference system. Defaults to "EPSG:4326".
        encoding (str, optional): The encoding of characters. Defaults to "utf-8".

    Returns:
        A GeoDataFrame.
    """

    check_package(name="geopandas", URL="https://geopandas.org")

    import geopandas as gpd
    import pandas as pd
    from shapely import wkt

    out_dir = os.getcwd()

    if geometry is None:
        out_geojson = os.path.join(out_dir, random_string() + ".geojson")
        csv_to_geojson(in_csv, out_geojson, latitude, longitude, encoding=encoding)

        gdf = gpd.read_file(out_geojson)
        os.remove(out_geojson)
    else:
        df = pd.read_csv(in_csv, encoding=encoding)
        df["geometry"] = df[geometry].apply(wkt.loads)
        gdf = gpd.GeoDataFrame(df, geometry="geometry", crs=crs, **kwargs)
    return gdf


def csv_to_vector(
    in_csv,
    output,
    latitude="latitude",
    longitude="longitude",
    geometry=None,
    crs="EPSG:4326",
    encoding="utf-8",
    **kwargs: Any,
):
    """Creates points for a CSV file and converts them to a vector dataset.

    Args:
        in_csv (str): The file path to the input CSV file.
        output (str): The file path to the output vector dataset.
        latitude (str, optional): The name of the column containing latitude coordinates. Defaults to "latitude".
        longitude (str, optional): The name of the column containing longitude coordinates. Defaults to "longitude".
        geometry (str, optional): The name of the column containing geometry. Defaults to None.
        crs (str, optional): The coordinate reference system. Defaults to "EPSG:4326".
        encoding (str, optional): The encoding of characters. Defaults to "utf-8".
        **kwargs: Additional keyword arguments to pass to gdf.to_file().

    """
    gdf = csv_to_gdf(in_csv, latitude, longitude, geometry, crs, encoding)
    gdf.to_file(output, **kwargs)


def create_code_cell(code: str = "", where: str = "below") -> None:
    """Creates a code cell in the IPython Notebook.

    Args:
        code (str, optional): Code to fill the new code cell with. Defaults to ''.
        where (str, optional): Where to add the new code cell. It can be one of the following: above, below, at_bottom. Defaults to 'below'.
    """

    import base64

    # try:
    #     import pyperclip
    # except ImportError:
    #     install_package("pyperclip")
    #     import pyperclip
    from IPython.display import Javascript, display

    # try:
    #     pyperclip.copy(str(code))
    # except Exception as e:
    #     pass

    encoded_code = (base64.b64encode(str.encode(code))).decode()
    display(
        Javascript(
            """
        var code = IPython.notebook.insert_cell_{0}('code');
        code.set_text(atob("{1}"));
    """.format(
                where, encoded_code
            )
        )
    )


def local_tile_pixel_value(
    lon,
    lat,
    tile_client,
    verbose=True,
    **kwargs: Any,
) -> Any:
    """Get pixel value from COG.

    Args:
        lon (float): Longitude of the pixel.
        lat (float): Latitude of the pixel.
        verbose (bool, optional): Print status messages. Defaults to True.

    Returns:
        rio-tiler point data.
    """
    return tile_client.point(lon, lat, coord_crs="EPSG:4326", **kwargs)


def local_tile_vmin_vmax(
    source,
    bands=None,
    **kwargs: Any,
) -> Tuple[float, float]:
    """Get vmin and vmax from COG.

    Args:
        source (str | TileClient): A local COG file path or TileClient object.
        bands (str | list, optional): A list of band names. Defaults to None.

    Raises:
        ValueError: If source is not a TileClient object or a local COG file path.

    Returns:
        A tuple of vmin and vmax.
    """
    check_package("localtileserver", "https://github.com/banesullivan/localtileserver")
    from localtileserver import TileClient

    if isinstance(source, str):
        tile_client = TileClient(source)
    elif isinstance(source, TileClient):
        tile_client = source
    else:
        raise ValueError("source must be a string or TileClient object.")

    bandnames = tile_client.band_names
    stats = tile_client.reader.statistics()

    if isinstance(bands, str):
        bands = [bands]
    elif isinstance(bands, list):
        pass
    elif bands is None:
        bands = bandnames

    if all(b in bandnames for b in bands):
        vmin = min([stats[b]["min"] for b in bands])
        vmax = max([stats[b]["max"] for b in bands])
    else:
        vmin = min([stats[b]["min"] for b in bandnames])
        vmax = max([stats[b]["max"] for b in bandnames])
    return vmin, vmax


def local_tile_bands(source: Union[str, Any]) -> List[str]:
    """Get band names from COG.

    Args:
        source (str | TileClient): A local COG file path or TileClient

    Returns:
        A list of band names.
    """
    check_package("localtileserver", "https://github.com/banesullivan/localtileserver")
    from localtileserver import TileClient

    if isinstance(source, str):
        tile_client = TileClient(source)
    elif isinstance(source, TileClient):
        tile_client = source
    else:
        raise ValueError("source must be a string or TileClient object.")

    return tile_client.band_names


def bbox_to_geojson(bounds) -> dict:
    """Convert coordinates of a bounding box to a geojson.

    Args:
        bounds (list | tuple): A list of coordinates representing [left, bottom, right, top] or m.bounds.

    Returns:
        A geojson feature.
    """

    if isinstance(bounds, tuple) and len(bounds) == 2:
        bounds = [bounds[0][1], bounds[0][0], bounds[1][1], bounds[1][0]]

    return {
        "geometry": {
            "type": "Polygon",
            "coordinates": [
                [
                    [bounds[0], bounds[3]],
                    [bounds[0], bounds[1]],
                    [bounds[2], bounds[1]],
                    [bounds[2], bounds[3]],
                    [bounds[0], bounds[3]],
                ]
            ],
        },
        "type": "Feature",
    }


def coords_to_geojson(coords: List[Any]) -> Dict[str, Any]:
    """Convert a list of bbox coordinates representing [left, bottom, right, top] to geojson FeatureCollection.

    Args:
        coords (list): A list of bbox coordinates representing [left, bottom, right, top].

    Returns:
        A geojson FeatureCollection.
    """

    features = []
    for bbox in coords:
        features.append(bbox_to_geojson(bbox))
    return {"type": "FeatureCollection", "features": features}


def explode(coords) -> Iterator[Any]:
    """Explode a GeoJSON geometry's coordinates object and yield
    coordinate tuples. As long as the input is conforming, the type of
    the geometry doesn't matter.  From Fiona 1.4.8

    Args:
        coords (list): A list of coordinates.

    Yields:
        Coordinate tuples extracted from the input.
    """

    for e in coords:
        if isinstance(e, (float, int)):
            yield coords
            break
        else:
            for f in explode(e):
                yield f


def get_bounds(
    geometry, north_up=True, transform=None
) -> Tuple[float, float, float, float]:
    """Bounding box of a GeoJSON geometry, GeometryCollection, or FeatureCollection.
    left, bottom, right, top
    *not* xmin, ymin, xmax, ymax
    If not north_up, y will be switched to guarantee the above.
    Source code adapted from https://github.com/mapbox/rasterio/blob/master/rasterio/features.py#L361

    Args:
        geometry (dict): A GeoJSON dict.
        north_up (bool, optional): . Defaults to True.
        transform ([type], optional): . Defaults to None.

    Returns:
        A list of coordinates representing [left, bottom, right, top].
    """

    if "bbox" in geometry:
        return tuple(geometry["bbox"])

    geometry = geometry.get("geometry") or geometry

    # geometry must be a geometry, GeometryCollection, or FeatureCollection
    if not (
        "coordinates" in geometry or "geometries" in geometry or "features" in geometry
    ):
        raise ValueError(
            "geometry must be a GeoJSON-like geometry, GeometryCollection, "
            "or FeatureCollection"
        )

    if "features" in geometry:
        # Input is a FeatureCollection
        xmins = []
        ymins = []
        xmaxs = []
        ymaxs = []
        for feature in geometry["features"]:
            xmin, ymin, xmax, ymax = get_bounds(feature["geometry"])
            xmins.append(xmin)
            ymins.append(ymin)
            xmaxs.append(xmax)
            ymaxs.append(ymax)
        if north_up:
            return min(xmins), min(ymins), max(xmaxs), max(ymaxs)
        else:
            return min(xmins), max(ymaxs), max(xmaxs), min(ymins)

    elif "geometries" in geometry:
        # Input is a geometry collection
        xmins = []
        ymins = []
        xmaxs = []
        ymaxs = []
        for geometry in geometry["geometries"]:
            xmin, ymin, xmax, ymax = get_bounds(geometry)
            xmins.append(xmin)
            ymins.append(ymin)
            xmaxs.append(xmax)
            ymaxs.append(ymax)
        if north_up:
            return min(xmins), min(ymins), max(xmaxs), max(ymaxs)
        else:
            return min(xmins), max(ymaxs), max(xmaxs), min(ymins)

    elif "coordinates" in geometry:
        # Input is a singular geometry object
        if transform is not None:
            xyz = list(explode(geometry["coordinates"]))
            xyz_px = [transform * point for point in xyz]
            xyz = tuple(zip(*xyz_px))
            return min(xyz[0]), max(xyz[1]), max(xyz[0]), min(xyz[1])
        else:
            xyz = tuple(zip(*list(explode(geometry["coordinates"]))))
            if north_up:
                return min(xyz[0]), min(xyz[1]), max(xyz[0]), max(xyz[1])
            else:
                return min(xyz[0]), max(xyz[1]), max(xyz[0]), min(xyz[1])

    # all valid inputs returned above, so whatever falls through is an error
    raise ValueError(
        "geometry must be a GeoJSON-like geometry, GeometryCollection, "
        "or FeatureCollection"
    )


def get_center(geometry, north_up=True, transform=None) -> Tuple[float, float]:
    """Get the centroid of a GeoJSON.

    Args:
        geometry (dict): A GeoJSON dict.
        north_up (bool, optional): . Defaults to True.
        transform ([type], optional): . Defaults to None.

    Returns:
        A coordinate pair in the form [lon, lat].
    """
    bounds = get_bounds(geometry, north_up, transform)
    center = ((bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2)  # (lat, lon)
    return center


def adjust_longitude(in_fc) -> Optional[dict]:
    """Adjusts longitude if it is less than -180 or greater than 180.

    Args:
        in_fc (dict): The input dictionary containing coordinates.

    Returns:
        A dictionary containing the converted longitudes.
    """
    try:
        keys = in_fc.keys()

        if "geometry" in keys:
            coordinates = in_fc["geometry"]["coordinates"]

            if in_fc["geometry"]["type"] == "Point":
                longitude = coordinates[0]
                if longitude < -180:
                    longitude = 360 + longitude
                elif longitude > 180:
                    longitude = longitude - 360
                in_fc["geometry"]["coordinates"][0] = longitude

            elif in_fc["geometry"]["type"] == "Polygon":
                for index1, item in enumerate(coordinates):
                    for index2, element in enumerate(item):
                        longitude = element[0]
                        if longitude < -180:
                            longitude = 360 + longitude
                        elif longitude > 180:
                            longitude = longitude - 360
                        in_fc["geometry"]["coordinates"][index1][index2][0] = longitude

            elif in_fc["geometry"]["type"] == "LineString":
                for index, element in enumerate(coordinates):
                    longitude = element[0]
                    if longitude < -180:
                        longitude = 360 + longitude
                    elif longitude > 180:
                        longitude = longitude - 360
                    in_fc["geometry"]["coordinates"][index][0] = longitude

        elif "type" in keys:
            coordinates = in_fc["coordinates"]

            if in_fc["type"] == "Point":
                longitude = coordinates[0]
                if longitude < -180:
                    longitude = 360 + longitude
                elif longitude > 180:
                    longitude = longitude - 360
                in_fc["coordinates"][0] = longitude

            elif in_fc["type"] == "Polygon":
                for index1, item in enumerate(coordinates):
                    for index2, element in enumerate(item):
                        longitude = element[0]
                        if longitude < -180:
                            longitude = 360 + longitude
                        elif longitude > 180:
                            longitude = longitude - 360
                        in_fc["coordinates"][index1][index2][0] = longitude

            elif in_fc["type"] == "LineString":
                for index, element in enumerate(coordinates):
                    longitude = element[0]
                    if longitude < -180:
                        longitude = 360 + longitude
                    elif longitude > 180:
                        longitude = longitude - 360
                    in_fc["coordinates"][index][0] = longitude

        return in_fc

    except Exception as e:
        print(e)
        return None


def is_GCS(in_shp):
    import pycrs

    if not os.path.exists(in_shp):
        raise FileNotFoundError("The input shapefile could not be found.")

    if not in_shp.endswith(".shp"):
        raise TypeError("The input shapefile is invalid.")

    in_prj = in_shp.replace(".shp", ".prj")

    if not os.path.exists(in_prj):
        warnings.warn(
            f"The projection file {in_prj} could not be found. Assuming the dataset is in a geographic coordinate system (GCS)."
        )
        return True
    else:
        with open(in_prj) as f:
            esri_wkt = f.read()
        epsg4326 = pycrs.parse.from_epsg_code(4326).to_proj4()
        try:
            crs = pycrs.parse.from_esri_wkt(esri_wkt).to_proj4()
            if crs == epsg4326:
                return True
            else:
                return False
        except Exception:
            return False


def kml_to_shp(in_kml, out_shp):
    """Converts a KML to shapefile.

    Args:
        in_kml (str): The file path to the input KML.
        out_shp (str): The file path to the output shapefile.

    Raises:
        FileNotFoundError: The input KML could not be found.
        TypeError: The output must be a shapefile.
    """

    warnings.filterwarnings("ignore")

    in_kml = os.path.abspath(in_kml)
    if not os.path.exists(in_kml):
        raise FileNotFoundError("The input KML could not be found.")

    out_shp = os.path.abspath(out_shp)
    if not out_shp.endswith(".shp"):
        raise TypeError("The output must be a shapefile.")

    out_dir = os.path.dirname(out_shp)
    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    check_package(name="geopandas", URL="https://geopandas.org")

    import fiona
    import geopandas as gpd

    # print(fiona.supported_drivers)
    fiona.drvsupport.supported_drivers["KML"] = "rw"
    df = gpd.read_file(in_kml, driver="KML")
    df.to_file(out_shp)


def kml_to_geojson(in_kml, out_geojson=None):
    """Converts a KML to GeoJSON.

    Args:
        in_kml (str): The file path to the input KML.
        out_geojson (str): The file path to the output GeoJSON. Defaults to None.

    Raises:
        FileNotFoundError: The input KML could not be found.
        TypeError: The output must be a GeoJSON.
    """

    warnings.filterwarnings("ignore")

    in_kml = os.path.abspath(in_kml)
    if not os.path.exists(in_kml):
        raise FileNotFoundError("The input KML could not be found.")

    if out_geojson is not None:
        out_geojson = os.path.abspath(out_geojson)
        ext = os.path.splitext(out_geojson)[1].lower()
        if ext not in [".json", ".geojson"]:
            raise TypeError("The output file must be a GeoJSON.")

        out_dir = os.path.dirname(out_geojson)
        if not os.path.exists(out_dir):
            os.makedirs(out_dir)

    check_package(name="geopandas", URL="https://geopandas.org")

    import fiona
    import geopandas as gpd

    # import fiona
    # print(fiona.supported_drivers)
    fiona.drvsupport.supported_drivers["KML"] = "rw"
    gdf = gpd.read_file(in_kml, driver="KML")

    if out_geojson is not None:
        gdf.to_file(out_geojson, driver="GeoJSON")
    else:
        return gdf.__geo_interface__


def csv_to_df(in_csv, **kwargs: Any) -> pd.DataFrame:
    """Converts a CSV file to pandas dataframe.

    Args:
        in_csv (str): File path to the input CSV.

    Returns:
        A pandas DataFrame.
    """
    import pandas as pd

    try:
        return pd.read_csv(in_csv, **kwargs)
    except Exception as e:
        raise Exception(e)


def shp_to_gdf(in_shp: str) -> "gpd.GeoDataFrame":
    """Converts a shapefile to Geopandas dataframe.

    Args:
        in_shp (str): File path to the input shapefile.

    Raises:
        FileNotFoundError: The provided shp could not be found.

    Returns:
        A GeoPandas GeoDataFrame.
    """

    warnings.filterwarnings("ignore")

    in_shp = os.path.abspath(in_shp)
    if not os.path.exists(in_shp):
        raise FileNotFoundError("The provided shp could not be found.")

    check_package(name="geopandas", URL="https://geopandas.org")

    import geopandas as gpd

    try:
        return gpd.read_file(in_shp)
    except Exception as e:
        raise Exception(e)


def shp_to_geojson(
    in_shp, output=None, encoding="utf-8", crs="EPSG:4326", **kwargs: Any
) -> Optional[dict]:
    """Converts a shapefile to GeoJSON.

    Args:
        in_shp (str): File path of the input shapefile.
        output (str, optional): File path of the output GeoJSON. Defaults to None.

    Returns:
        The JSON object representing the shapefile.
    """
    try:
        import geopandas as gpd

        gdf = gpd.read_file(in_shp, **kwargs)
        gdf.to_crs(crs, inplace=True)
        if output is None:
            return gdf.__geo_interface__
        else:
            gdf.to_file(output, driver="GeoJSON")
    except Exception as e:
        raise Exception(e)


def delete_shp(in_shp: str, verbose: bool = False) -> None:
    """Deletes a shapefile.

    Args:
        in_shp (str): The input shapefile to delete.
        verbose (bool, optional): Whether to print out descriptive text. Defaults to True.
    """
    from pathlib import Path

    in_shp = os.path.abspath(in_shp)
    in_dir = os.path.dirname(in_shp)
    basename = os.path.basename(in_shp).replace(".shp", "")

    files = Path(in_dir).rglob(basename + ".*")

    for file in files:
        filepath = os.path.join(in_dir, str(file))
        os.remove(filepath)
        if verbose:
            print(f"Deleted {filepath}")


def vector_to_geojson(
    filename,
    out_geojson=None,
    bbox=None,
    mask=None,
    rows=None,
    epsg="4326",
    encoding="utf-8",
    **kwargs: Any,
) -> Optional[dict]:
    """Converts any geopandas-supported vector dataset to GeoJSON.

    Args:
        filename (str): Either the absolute or relative path to the file or URL to be opened, or any object with a read() method (such as an open file or StringIO).
        out_geojson (str, optional): The file path to the output GeoJSON. Defaults to None.
        bbox (tuple | GeoDataFrame or GeoSeries | shapely Geometry, optional): Filter features by given bounding box, GeoSeries, GeoDataFrame or a shapely geometry. CRS mis-matches are resolved if given a GeoSeries or GeoDataFrame. Cannot be used with mask. Defaults to None.
        mask (dict | GeoDataFrame or GeoSeries | shapely Geometry, optional): Filter for features that intersect with the given dict-like geojson geometry, GeoSeries, GeoDataFrame or shapely geometry. CRS mis-matches are resolved if given a GeoSeries or GeoDataFrame. Cannot be used with bbox. Defaults to None.
        rows (int or slice, optional): Load in specific rows by passing an integer (first n rows) or a slice() object.. Defaults to None.
        epsg (str, optional): The EPSG number to convert to. Defaults to "4326".
        encoding (str, optional): The encoding of the input file. Defaults to "utf-8".


    Raises:
        ValueError: When the output file path is invalid.

    Returns:
        A dictionary containing the GeoJSON.
    """

    warnings.filterwarnings("ignore")
    check_package(name="geopandas", URL="https://geopandas.org")
    import fiona
    import geopandas as gpd

    if not filename.startswith("http"):
        filename = os.path.abspath(filename)
        if filename.endswith(".zip"):
            filename = "zip://" + filename
    ext = os.path.splitext(filename)[1].lower()
    if ext == ".kml":
        fiona.drvsupport.supported_drivers["KML"] = "rw"
        df = gpd.read_file(
            filename,
            bbox=bbox,
            mask=mask,
            rows=rows,
            driver="KML",
            encoding=encoding,
            **kwargs,
        )
    else:
        df = gpd.read_file(
            filename, bbox=bbox, mask=mask, rows=rows, encoding=encoding, **kwargs
        )
    gdf = df.to_crs(epsg=epsg)

    if out_geojson is not None:
        if not out_geojson.lower().endswith(".geojson"):
            raise ValueError("The output file must have a geojson file extension.")

        out_geojson = os.path.abspath(out_geojson)
        out_dir = os.path.dirname(out_geojson)
        if not os.path.exists(out_dir):
            os.makedirs(out_dir)

        gdf.to_file(out_geojson, driver="GeoJSON")

    else:
        return gdf.__geo_interface__


def screen_capture(outfile: str, monitor: int = 1) -> Optional[str]:
    """Takes a full screenshot of the selected monitor.

    Args:
        outfile (str): The output file path to the screenshot.
        monitor (int, optional): The monitor to take the screenshot. Defaults to 1.
    """
    try:
        from mss import mss
    except ImportError:
        raise ImportError("Please install mss using 'pip install mss'")

    out_dir = os.path.dirname(outfile)
    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    if not isinstance(monitor, int):
        print("The monitor number must be an integer.")
        return

    try:
        with mss() as sct:
            sct.shot(output=outfile, mon=monitor)
            return outfile

    except Exception as e:
        raise Exception(e)


def gdf_to_geojson(
    gdf, out_geojson=None, epsg=None, tuple_to_list=False, encoding="utf-8"
) -> Optional[dict]:
    """Converts a GeoDataFame to GeoJSON.

    Args:
        gdf (GeoDataFrame): A GeoPandas GeoDataFrame.
        out_geojson (str, optional): File path to he output GeoJSON. Defaults to None.
        epsg (str, optional): An EPSG string, e.g., "4326". Defaults to None.
        tuple_to_list (bool, optional): Whether to convert tuples to lists. Defaults to False.
        encoding (str, optional): The encoding to use for the GeoJSON. Defaults to "utf-8".

    Raises:
        TypeError: When the output file extension is incorrect.
        Exception: When the conversion fails.

    Returns:
        When out_json is None returns a dict.
    """
    check_package(name="geopandas", URL="https://geopandas.org")

    def listit(t):
        return list(map(listit, t)) if isinstance(t, (list, tuple)) else t

    try:
        if epsg is not None:
            if gdf.crs is not None and gdf.crs.to_epsg() != epsg:
                gdf = gdf.to_crs(epsg=epsg)
        geojson = gdf.__geo_interface__

        if tuple_to_list:
            for feature in geojson["features"]:
                feature["geometry"]["coordinates"] = listit(
                    feature["geometry"]["coordinates"]
                )

        if out_geojson is None:
            return geojson
        else:
            ext = os.path.splitext(out_geojson)[1]
            if ext.lower() not in [".json", ".geojson"]:
                raise TypeError(
                    "The output file extension must be either .json or .geojson"
                )
            out_dir = os.path.dirname(out_geojson)
            if not os.path.exists(out_dir):
                os.makedirs(out_dir)

            gdf.to_file(out_geojson, driver="GeoJSON", encoding=encoding)
    except Exception as e:
        raise Exception(e)


def connect_postgis(
    database, host="localhost", user=None, password=None, port=5432, use_env_var=False
) -> Any:
    """Connects to a PostGIS database.

    Args:
        database (str): Name of the database
        host (str, optional): Hosting server for the database. Defaults to "localhost".
        user (str, optional): User name to access the database. Defaults to None.
        password (str, optional): Password to access the database. Defaults to None.
        port (int, optional): Port number to connect to at the server host. Defaults to 5432.
        use_env_var (bool, optional): Whether to use environment variables. It set to True, user and password are treated as an environment variables with default values user="SQL_USER" and password="SQL_PASSWORD". Defaults to False.

    Raises:
        ValueError: If user is not specified.
        ValueError: If password is not specified.

    Returns:
        A SQLAlchemy engine connection string or engine object.
    """
    check_package(name="geopandas", URL="https://geopandas.org")
    check_package(
        name="sqlalchemy",
        URL="https://docs.sqlalchemy.org/en/14/intro.html#installation",
    )

    from sqlalchemy import create_engine

    if use_env_var:
        if user is not None:
            user = os.getenv(user)
        else:
            user = os.getenv("SQL_USER")

        if password is not None:
            password = os.getenv(password)
        else:
            password = os.getenv("SQL_PASSWORD")

        if user is None:
            raise ValueError("user is not specified.")
        if password is None:
            raise ValueError("password is not specified.")

    connection_string = f"postgresql://{user}:{password}@{host}:{port}/{database}"
    engine = create_engine(connection_string)

    return engine


def read_postgis(sql, con, geom_col="geom", crs=None, **kwargs: Any) -> Any:
    """Reads data from a PostGIS database and returns a GeoDataFrame.

    Args:
        sql (str): SQL query to execute in selecting entries from database, or name of the table to read from the database.
        con (sqlalchemy.engine.Engine): Active connection to the database to query.
        geom_col (str, optional): Column name to convert to shapely geometries. Defaults to "geom".
        crs (str | dict, optional): CRS to use for the returned GeoDataFrame; if not set, tries to determine CRS from the SRID associated with the first geometry in the database, and assigns that to all geometries. Defaults to None.

    Returns:
        [type]: [description]
    """
    check_package(name="geopandas", URL="https://geopandas.org")

    import geopandas as gpd

    gdf = gpd.read_postgis(sql, con, geom_col, crs, **kwargs)
    return gdf


def vector_col_names(filename, **kwargs: Any) -> List[str]:
    """Retrieves the column names of a vector attribute table.

    Args:
        filename (str): The input file path.

    Returns:
        The list of column names.
    """

    warnings.filterwarnings("ignore")
    check_package(name="geopandas", URL="https://geopandas.org")
    import fiona
    import geopandas as gpd

    if not filename.startswith("http"):
        filename = os.path.abspath(filename)
    ext = os.path.splitext(filename)[1].lower()
    if ext == ".kml":
        fiona.drvsupport.supported_drivers["KML"] = "rw"
        gdf = gpd.read_file(filename, driver="KML", **kwargs)
    else:
        gdf = gpd.read_file(filename, **kwargs)
    col_names = gdf.columns.values.tolist()
    return col_names


def get_api_key(name: Optional[str] = None, key: Optional[str] = None) -> Optional[str]:
    """
    Retrieves an API key. If a key is provided, it is returned directly. If a
    name is provided, the function attempts to retrieve the key from user data
    (if running in Google Colab) or from environment variables.

    Args:
        name (Optional[str], optional): The name of the key to retrieve. Defaults to None.
        key (Optional[str], optional): The key to return directly. Defaults to None.

    Returns:
        The retrieved key, or None if no key was found.
    """
    if key is not None:
        return key
    if name is not None:
        try:
            if _in_colab_shell():
                from google.colab import userdata  # pylint: disable=E0611

                return userdata.get(name)
        except Exception:
            pass
        return os.environ.get(name)
    return None


def set_api_key(key: str, name: str = "GOOGLE_MAPS_API_KEY"):
    """Sets the Google Maps API key. You can generate one from https://bit.ly/3sw0THG.

    Args:
        key (str): The Google Maps API key.
        name (str, optional): The name of the environment variable. Defaults to "GOOGLE_MAPS_API_KEY".
    """
    os.environ[name] = key


def planet_monthly_tropical(api_key=None, token_name="PLANET_API_KEY") -> List[str]:
    """Generates Planet monthly imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Raises:
        ValueError: If the API key could not be found.

    Returns:
        A list of tile URLs.
    """
    from datetime import date

    if api_key is None:
        api_key = os.environ.get(token_name)
        if api_key is None:
            raise ValueError("The Planet API Key must be provided.")

    today = date.today()
    year_now = int(today.strftime("%Y"))
    month_now = int(today.strftime("%m"))

    links = []
    prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/planet_medres_normalized_analytic_"
    subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="

    for year in range(2020, year_now + 1):
        for month in range(1, 13):
            m_str = str(year) + "-" + str(month).zfill(2)

            if year == 2020 and month < 9:
                continue
            if year == year_now and month >= month_now:
                break

            url = f"{prefix}{m_str}{subfix}{api_key}"
            links.append(url)

    return links


def planet_biannual_tropical(api_key=None, token_name="PLANET_API_KEY") -> List[str]:
    """Generates Planet bi-annual imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Raises:
        ValueError: If the API key could not be found.

    Returns:
        A list of tile URLs.
    """

    if api_key is None:
        api_key = os.environ.get(token_name)
        if api_key is None:
            raise ValueError("The Planet API Key must be provided.")

    dates = [
        "2015-12_2016-05",
        "2016-06_2016-11",
        "2016-12_2017-05",
        "2017-06_2017-11",
        "2017-12_2018-05",
        "2018-06_2018-11",
        "2018-12_2019-05",
        "2019-06_2019-11",
        "2019-12_2020-05",
        "2020-06_2020-08",
    ]

    link = []
    prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/planet_medres_normalized_analytic_"
    subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="

    for d in dates:
        url = f"{prefix}{d}{subfix}{api_key}"
        link.append(url)

    return link


def planet_catalog_tropical(api_key=None, token_name="PLANET_API_KEY") -> List[str]:
    """Generates Planet bi-annual and monthly imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Returns:
        list: A list of tile URLs.
    """
    biannual = planet_biannual_tropical(api_key, token_name)
    monthly = planet_monthly_tropical(api_key, token_name)
    return biannual + monthly


def planet_monthly_tiles_tropical(
    api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"
):
    """Generates Planet  monthly imagery TileLayer based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        A dictionary of TileLayer.
    """

    if tile_format not in ["ipyleaflet", "folium"]:
        raise ValueError("The tile format must be either ipyleaflet or folium.")

    tiles = {}
    link = planet_monthly_tropical(api_key, token_name)
    for url in link:
        index = url.find("20")
        name = "Planet_" + url[index : index + 7]

        if tile_format == "ipyleaflet":
            tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
        else:
            tile = folium.TileLayer(
                tiles=url,
                attr="Planet",
                name=name,
                overlay=True,
                control=True,
            )

        tiles[name] = tile

    return tiles


def planet_biannual_tiles_tropical(
    api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"
):
    """Generates Planet  bi-annual imagery TileLayer based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        A dictionary of TileLayer.
    """

    if tile_format not in ["ipyleaflet", "folium"]:
        raise ValueError("The tile format must be either ipyleaflet or folium.")

    tiles = {}
    link = planet_biannual_tropical(api_key, token_name)
    for url in link:
        index = url.find("20")
        name = "Planet_" + url[index : index + 15]
        if tile_format == "ipyleaflet":
            tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
        else:
            tile = folium.TileLayer(
                tiles=url,
                attr="Planet",
                name=name,
                overlay=True,
                control=True,
            )
        tiles[name] = tile

    return tiles


def planet_tiles_tropical(
    api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"
) -> Dict[str, Any]:
    """Generates Planet  monthly imagery TileLayer based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        dict: A dictionary of TileLayer.
    """

    catalog = {}
    biannul = planet_biannual_tiles_tropical(api_key, token_name, tile_format)
    monthly = planet_monthly_tiles_tropical(api_key, token_name, tile_format)

    for key in biannul:
        catalog[key] = biannul[key]

    for key in monthly:
        catalog[key] = monthly[key]

    return catalog


def planet_monthly(api_key=None, token_name="PLANET_API_KEY") -> List[str]:
    """Generates Planet monthly imagery URLs based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Raises:
        ValueError: If the API key could not be found.

    Returns:
        list: A list of tile URLs.
    """
    from datetime import date

    if api_key is None:
        api_key = os.environ.get(token_name)
        if api_key is None:
            raise ValueError("The Planet API Key must be provided.")

    today = date.today()
    year_now = int(today.strftime("%Y"))
    month_now = int(today.strftime("%m"))

    link = []
    prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/global_monthly_"
    subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="

    for year in range(2016, year_now + 1):
        for month in range(1, 13):
            m_str = str(year) + "_" + str(month).zfill(2)

            if year == year_now and month >= month_now:
                break

            url = f"{prefix}{m_str}{subfix}{api_key}"
            link.append(url)

    return link


def planet_quarterly(api_key=None, token_name="PLANET_API_KEY") -> List[str]:
    """Generates Planet quarterly imagery URLs based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Raises:
        ValueError: If the API key could not be found.

    Returns:
        list: A list of tile URLs.
    """
    from datetime import date

    if api_key is None:
        api_key = os.environ.get(token_name)
        if api_key is None:
            raise ValueError("The Planet API Key must be provided.")

    today = date.today()
    year_now = int(today.strftime("%Y"))
    month_now = int(today.strftime("%m"))
    quarter_now = (month_now - 1) // 3 + 1

    link = []
    prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/global_quarterly_"
    subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="

    for year in range(2016, year_now + 1):
        for quarter in range(1, 5):
            m_str = str(year) + "q" + str(quarter)

            if year == year_now and quarter >= quarter_now:
                break

            url = f"{prefix}{m_str}{subfix}{api_key}"
            link.append(url)

    return link


def planet_catalog(api_key=None, token_name="PLANET_API_KEY") -> List[str]:
    """Generates Planet bi-annual and monthly imagery URLs based on an API key. See https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Returns:
        list: A list of tile URLs.
    """
    quarterly = planet_quarterly(api_key, token_name)
    monthly = planet_monthly(api_key, token_name)
    return quarterly + monthly


def planet_monthly_tiles(
    api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"
) -> Dict[str, Any]:
    """Generates Planet monthly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        dict: A dictionary of TileLayer.
    """

    if tile_format not in ["ipyleaflet", "folium"]:
        raise ValueError("The tile format must be either ipyleaflet or folium.")

    tiles = {}
    link = planet_monthly(api_key, token_name)

    for url in link:
        index = url.find("20")
        name = "Planet_" + url[index : index + 7]

        if tile_format == "ipyleaflet":
            tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
        else:
            tile = folium.TileLayer(
                tiles=url,
                attr="Planet",
                name=name,
                overlay=True,
                control=True,
            )

        tiles[name] = tile

    return tiles


def planet_quarterly_tiles(
    api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"
):
    """Generates Planet  quarterly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        dict: A dictionary of TileLayer.
    """

    if tile_format not in ["ipyleaflet", "folium"]:
        raise ValueError("The tile format must be either ipyleaflet or folium.")

    tiles = {}
    links = planet_quarterly(api_key, token_name)

    for url in links:
        index = url.find("20")
        name = "Planet_" + url[index : index + 6]

        if tile_format == "ipyleaflet":
            tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
        else:
            tile = folium.TileLayer(
                tiles=url,
                attr="Planet",
                name=name,
                overlay=True,
                control=True,
            )

        tiles[name] = tile

    return tiles


def planet_tiles(api_key=None, token_name="PLANET_API_KEY", tile_format="ipyleaflet"):
    """Generates Planet imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

    Args:
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        dict: A dictionary of TileLayer.
    """

    catalog = {}
    quarterly = planet_quarterly_tiles(api_key, token_name, tile_format)
    monthly = planet_monthly_tiles(api_key, token_name, tile_format)

    for key in quarterly:
        catalog[key] = quarterly[key]

    for key in monthly:
        catalog[key] = monthly[key]

    return catalog


def planet_by_quarter(
    year=2016,
    quarter=1,
    api_key=None,
    token_name="PLANET_API_KEY",
):
    """Gets Planet global mosaic tile url by quarter. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

    Args:
        year (int, optional): The year of Planet global mosaic, must be >=2016. Defaults to 2016.
        quarter (int, optional): The quarter of Planet global mosaic, must be 1-4. Defaults to 1.
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Raises:
        ValueError: The Planet API key is not provided.
        ValueError: The year is invalid.
        ValueError: The quarter is invalid.
        ValueError: The quarter is invalid.

    Returns:
        A Planet global mosaic tile URL.
    """
    from datetime import date

    if api_key is None:
        api_key = os.environ.get(token_name)
        if api_key is None:
            raise ValueError("The Planet API Key must be provided.")

    today = date.today()
    year_now = int(today.strftime("%Y"))
    month_now = int(today.strftime("%m"))
    quarter_now = (month_now - 1) // 3 + 1

    if year > year_now:
        raise ValueError(f"Year must be between 2016 and {year_now}.")
    elif year == year_now and quarter >= quarter_now:
        raise ValueError(f"Quarter must be less than {quarter_now} for year {year_now}")

    if quarter < 1 or quarter > 4:
        raise ValueError("Quarter must be between 1 and 4.")

    prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/global_quarterly_"
    subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="

    m_str = str(year) + "q" + str(quarter)
    url = f"{prefix}{m_str}{subfix}{api_key}"

    return url


def planet_by_month(
    year=2016,
    month=1,
    api_key=None,
    token_name="PLANET_API_KEY",
):
    """Gets Planet global mosaic tile url by month. To get a Planet API key, see https://developers.planet.com/quickstart/apis/

    Args:
        year (int, optional): The year of Planet global mosaic, must be >=2016. Defaults to 2016.
        month (int, optional): The month of Planet global mosaic, must be 1-12. Defaults to 1.
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".

    Raises:
        ValueError: The Planet API key is not provided.
        ValueError: The year is invalid.
        ValueError: The month is invalid.
        ValueError: The month is invalid.

    Returns:
        A Planet global mosaic tile URL.
    """
    from datetime import date

    if api_key is None:
        api_key = os.environ.get(token_name)
        if api_key is None:
            raise ValueError("The Planet API Key must be provided.")

    today = date.today()
    year_now = int(today.strftime("%Y"))
    month_now = int(today.strftime("%m"))
    # quarter_now = (month_now - 1) // 3 + 1

    if year > year_now:
        raise ValueError(f"Year must be between 2016 and {year_now}.")
    elif year == year_now and month >= month_now:
        raise ValueError(f"Month must be less than {month_now} for year {year_now}")

    if month < 1 or month > 12:
        raise ValueError("Month must be between 1 and 12.")

    prefix = "https://tiles.planet.com/basemaps/v1/planet-tiles/global_monthly_"
    subfix = "_mosaic/gmap/{z}/{x}/{y}.png?api_key="

    m_str = str(year) + "_" + str(month).zfill(2)
    url = f"{prefix}{m_str}{subfix}{api_key}"

    return url


def planet_tile_by_quarter(
    year=2016,
    quarter=1,
    name=None,
    api_key=None,
    token_name="PLANET_API_KEY",
    tile_format="ipyleaflet",
):
    """Generates Planet quarterly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis

    Args:
        year (int, optional): The year of Planet global mosaic, must be >=2016. Defaults to 2016.
        quarter (int, optional): The quarter of Planet global mosaic, must be 1-4. Defaults to 1.
        name (str, optional): The layer name to use. Defaults to None.
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        dict: A dictionary of TileLayer.
    """

    if tile_format not in ["ipyleaflet", "folium"]:
        raise ValueError("The tile format must be either ipyleaflet or folium.")

    url = planet_by_quarter(year, quarter, api_key, token_name)

    if name is None:
        name = "Planet_" + str(year) + "_q" + str(quarter)

    if tile_format == "ipyleaflet":
        tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
    else:
        tile = folium.TileLayer(
            tiles=url,
            attr="Planet",
            name=name,
            overlay=True,
            control=True,
        )

    return tile


def planet_tile_by_month(
    year=2016,
    month=1,
    name=None,
    api_key=None,
    token_name="PLANET_API_KEY",
    tile_format="ipyleaflet",
):
    """Generates Planet monthly imagery TileLayer based on an API key. To get a Planet API key, see https://developers.planet.com/quickstart/apis

    Args:
        year (int, optional): The year of Planet global mosaic, must be >=2016. Defaults to 2016.
        month (int, optional): The month of Planet global mosaic, must be 1-12. Defaults to 1.
        name (str, optional): The layer name to use. Defaults to None.
        api_key (str, optional): The Planet API key. Defaults to None.
        token_name (str, optional): The environment variable name of the API key. Defaults to "PLANET_API_KEY".
        tile_format (str, optional): The TileLayer format, can be either ipyleaflet or folium. Defaults to "ipyleaflet".

    Raises:
        ValueError: If the tile layer format is invalid.

    Returns:
        dict: A dictionary of TileLayer.
    """

    if tile_format not in ["ipyleaflet", "folium"]:
        raise ValueError("The tile format must be either ipyleaflet or folium.")

    url = planet_by_month(year, month, api_key, token_name)

    if name is None:
        name = "Planet_" + str(year) + "_" + str(month).zfill(2)

    if tile_format == "ipyleaflet":
        tile = ipyleaflet.TileLayer(url=url, attribution="Planet", name=name)
    else:
        tile = folium.TileLayer(
            tiles=url,
            attr="Planet",
            name=name,
            overlay=True,
            control=True,
        )

    return tile


def basemap_xyz_tiles() -> Dict[str, Any]:
    """Returns a dictionary containing a set of basemaps that are XYZ tile layers.

    Returns:
        A dictionary of XYZ tile layers.
    """
    from .leafmap import basemaps

    layers_dict = {}
    keys = dict(basemaps).keys()
    for key in keys:
        if isinstance(basemaps[key], ipyleaflet.WMSLayer):
            pass
        else:
            layers_dict[key] = basemaps[key]
    return layers_dict


def to_hex_colors(colors):
    """Adds # to a list of hex color codes.

    Args:
        colors (list): A list of hex color codes.

    Returns:
        A list of hex color codes prefixed with #.
    """
    result = all([len(color.strip()) == 6 for color in colors])
    if result:
        return ["#" + color.strip() for color in colors]
    else:
        return colors


def get_census_dict(reset=False):
    """Returns a dictionary of Census data.

    Args:
        reset (bool, optional): Reset the dictionary. Defaults to False.

    Returns:
        A dictionary of Census data.
    """
    import importlib.resources
    import json

    pkg_dir = os.path.dirname(importlib.resources.files("leafmap") / "leafmap.py")
    census_data = os.path.join(pkg_dir, "data/census_data.json")

    if reset:
        try:
            from owslib.wms import WebMapService
        except ImportError:
            raise ImportError("Please install owslib using 'pip install owslib'.")

        census_dict = {}

        names = [
            "Current",
            "ACS 2021",
            "ACS 2019",
            "ACS 2018",
            "ACS 2017",
            "ACS 2016",
            "ACS 2015",
            "ACS 2014",
            "ACS 2013",
            "ACS 2012",
            "ECON 2012",
            "Census 2020",
            "Census 2010",
            "Physical Features",
            "Decennial Census 2020",
            "Decennial Census 2010",
            "Decennial Census 2000",
            "Decennial Physical Features",
        ]

        links = {}

        print("Retrieving data. Please wait ...")
        for name in names:
            if "Decennial" not in name:
                links[name] = (
                    f"https://tigerweb.geo.census.gov/arcgis/services/TIGERweb/tigerWMS_{name.replace(' ', '')}/MapServer/WMSServer"
                )
            else:
                links[name] = (
                    f"https://tigerweb.geo.census.gov/arcgis/services/Census2020/tigerWMS_{name.replace('Decennial', '').replace(' ', '')}/MapServer/WMSServer"
                )

            wms = WebMapService(links[name], timeout=300)
            layers = list(wms.contents)
            layers.sort()
            census_dict[name] = {
                "url": links[name],
                "layers": layers,
                # "title": wms.identification.title,
                # "abstract": wms.identification.abstract,
            }

        with open(census_data, "w") as f:
            json.dump(census_dict, f, indent=4)

    else:
        with open(census_data, "r") as f:
            census_dict = json.load(f)

    return census_dict


def search_xyz_services(keyword, name=None, list_only=True, add_prefix=True):
    """Search for XYZ tile providers from xyzservices.

    Args:
        keyword (str): The keyword to search for.
        name (str, optional): The name of the xyz tile. Defaults to None.
        list_only (bool, optional): If True, only the list of services will be returned. Defaults to True.
        add_prefix (bool, optional): If True, the prefix "xyz." will be added to the service name. Defaults to True.

    Returns:
        A list of XYZ tile providers.
    """

    import xyzservices.providers as xyz

    if name is None:
        providers = xyz.filter(keyword=keyword).flatten()
    else:
        providers = xyz.filter(name=name).flatten()

    if list_only:
        if add_prefix:
            return ["xyz." + provider for provider in providers]
        else:
            return [provider for provider in providers]
    else:
        return providers


def search_qms(keyword, limit=10, list_only=True, add_prefix=True):
    """Search for QMS tile providers from Quick Map Services.

    Args:
        keyword (str): The keyword to search for.
        limit (int, optional): The maximum number of results to return. Defaults to 10.
        list_only (bool, optional): If True, only the list of services will be returned. Defaults to True.
        add_prefix (bool, optional): If True, the prefix "qms." will be added to the service name. Defaults to True.

    Returns:
        A list of QMS tile providers.
    """

    QMS_API = "https://qms.nextgis.com/api/v1/geoservices"
    services = requests.get(
        f"{QMS_API}/?search={keyword}&type=tms&epsg=3857&limit={limit}"
    )
    services = services.json()
    if services["results"]:
        providers = services["results"]
        if list_only:
            if add_prefix:
                return ["qms." + provider["name"] for provider in providers]
            else:
                return [provider["name"] for provider in providers]
        else:
            return providers
    else:
        return None


def get_wms_layers(url):
    """Returns a list of WMS layers from a WMS service.

    Args:
        url (str): The URL of the WMS service.

    Returns:
        A list of WMS layers.
    """
    try:
        from owslib.wms import WebMapService
    except ImportError:
        raise ImportError("Please install owslib using 'pip install owslib'.")

    wms = WebMapService(url)
    layers = list(wms.contents)
    layers.sort()
    return layers


def streamlit_legend(html, width=None, height=None, scrolling=True):
    """Streamlit function to display a legend.

    Args:
        html (str): The HTML string of the legend.
        width (str, optional): The width of the legend. Defaults to None.
        height (str, optional): The height of the legend. Defaults to None.
        scrolling (bool, optional): Whether to allow scrolling in the legend. Defaults to True.

    """

    try:
        import streamlit.components.v1 as components

        components.html(html, width=width, height=height, scrolling=scrolling)

    except ImportError:
        print("Streamlit is not installed. Please run 'pip install streamlit'.")
        return


def read_file_from_url(url, return_type="list", encoding="utf-8"):
    """Reads a file from a URL.

    Args:
        url (str): The URL of the file.
        return_type (str, optional): The return type, can either be string or list. Defaults to "list".
        encoding (str, optional): The encoding of the file. Defaults to "utf-8".

    Raises:
        ValueError: The return type must be either list or string.

    Returns:
        The contents of the file as a list or string depending on return_type.
    """
    from urllib.request import urlopen

    if return_type == "list":
        return [line.decode(encoding).rstrip() for line in urlopen(url).readlines()]
    elif return_type == "string":
        return urlopen(url).read().decode(encoding)
    else:
        raise ValueError("The return type must be either list or string.")


def st_download_button(
    label,
    data,
    file_name=None,
    mime=None,
    key=None,
    help=None,
    on_click=None,
    args=None,
    csv_sep=",",
    **kwargs: Any,
):
    """Streamlit function to create a download button.

    Args:
        label (str): A short label explaining to the user what this button is for..
        data (str | list): The contents of the file to be downloaded. See example below for caching techniques to avoid recomputing this data unnecessarily.
        file_name (str, optional): An optional string to use as the name of the file to be downloaded, such as 'my_file.csv'. If not specified, the name will be automatically generated. Defaults to None.
        mime (str, optional): The MIME type of the data. If None, defaults to "text/plain" (if data is of type str or is a textual file) or "application/octet-stream" (if data is of type bytes or is a binary file). Defaults to None.
        key (str, optional): An optional string or integer to use as the unique key for the widget. If this is omitted, a key will be generated for the widget based on its content. Multiple widgets of the same type may not share the same key. Defaults to None.
        help (str, optional): An optional tooltip that gets displayed when the button is hovered over. Defaults to None.
        on_click (str, optional): An optional callback invoked when this button is clicked. Defaults to None.
        args (list, optional): An optional tuple of args to pass to the callback. Defaults to None.
        kwargs (dict, optional): An optional tuple of args to pass to the callback.

    """
    try:
        import pandas as pd
        import streamlit as st

        if key is None:
            key = random_string(6)

        if isinstance(data, str):
            if file_name is None:
                file_name = data.split("/")[-1]

            if data.endswith(".csv"):
                data = pd.read_csv(data).to_csv(sep=csv_sep, index=False)
                if mime is None:
                    mime = "text/csv"
                return st.download_button(
                    label, data, file_name, mime, key, help, on_click, args, **kwargs
                )
            elif (
                data.endswith(".gif") or data.endswith(".png") or data.endswith(".jpg")
            ):
                if mime is None:
                    mime = f"image/{os.path.splitext(data)[1][1:]}"

                with open(data, "rb") as file:
                    return st.download_button(
                        label,
                        file,
                        file_name,
                        mime,
                        key,
                        help,
                        on_click,
                        args,
                        **kwargs,
                    )
        elif isinstance(data, pd.DataFrame):
            if file_name is None:
                file_name = "data.csv"

            data = data.to_csv(sep=csv_sep, index=False)
            if mime is None:
                mime = "text/csv"
            return st.download_button(
                label, data, file_name, mime, key, help, on_click, args, **kwargs
            )

        else:
            # if mime is None:
            #     mime = "application/pdf"
            return st.download_button(
                label,
                data,
                file_name,
                mime,
                key,
                help,
                on_click,
                args,
                **kwargs,
            )

    except ImportError:
        print("Streamlit is not installed. Please run 'pip install streamlit'.")
        return
    except Exception as e:
        raise Exception(e)


def save_data(data, file_ext=None, file_name=None):
    """Save data in the memory to a file.

    Args:
        data (object): The data to be saved.
        file_ext (str): The file extension of the file.
        file_name (str, optional): The name of the file to be saved. Defaults to None.

    Returns:
        The path of the file.
    """
    import tempfile
    import uuid

    try:
        if file_ext is None:
            if hasattr(data, "name"):
                _, file_ext = os.path.splitext(data.name)
        else:
            if not file_ext.startswith("."):
                file_ext = "." + file_ext

        if file_name is not None:
            file_path = os.path.abspath(file_name)
            if not file_path.endswith(file_ext):
                file_path = file_path + file_ext
        else:
            file_id = str(uuid.uuid4())
            file_path = os.path.join(tempfile.gettempdir(), f"{file_id}{file_ext}")

        with open(file_path, "wb") as file:
            file.write(data.getbuffer())
        return file_path
    except Exception as e:
        print(e)
        return None


def temp_file_path(extension):
    """Returns a temporary file path.

    Args:
        extension (str): The file extension.

    Returns:
        The temporary file path.
    """

    import tempfile
    import uuid

    if not extension.startswith("."):
        extension = "." + extension
    file_id = str(uuid.uuid4())
    file_path = os.path.join(tempfile.gettempdir(), f"{file_id}{extension}")

    return file_path


def get_local_tile_layer(
    source,
    port="default",
    debug=False,
    indexes=None,
    colormap=None,
    vmin=None,
    vmax=None,
    nodata=None,
    attribution=None,
    tile_format="ipyleaflet",
    layer_name="Local COG",
    client_args={"cors_all": False},
    return_client=False,
    quiet=False,
    **kwargs: Any,
):
    """Generate an ipyleaflet/folium TileLayer from a local raster dataset or remote Cloud Optimized GeoTIFF (COG).
        If you are using this function in JupyterHub on a remote server and the raster does not render properly, try
        running the following two lines before calling this function:

        import os
        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'

    Args:
        source (str): The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.
        port (str, optional): The port to use for the server. Defaults to "default".
        debug (bool, optional): If True, the server will be started in debug mode. Defaults to False.
        indexes (int, optional): The band(s) to use. Band indexing starts at 1. Defaults to None.
        colormap (str, optional): The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.
        vmin (float, optional): The minimum value to use when colormapping the colormap when plotting a single band. Defaults to None.
        vmax (float, optional): The maximum value to use when colormapping the colormap when plotting a single band. Defaults to None.
        nodata (float, optional): The value from the band to use to interpret as not valid data. Defaults to None.
        attribution (str, optional): Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.
        tile_format (str, optional): The tile layer format. Can be either ipyleaflet or folium. Defaults to "ipyleaflet".
        layer_name (str, optional): The layer name to use. Defaults to None.
        client_args (dict, optional): Additional arguments to pass to the TileClient. Defaults to {}.
        return_client (bool, optional): If True, the tile client will be returned. Defaults to False.
        quiet (bool, optional): If True, the error messages will be suppressed. Defaults to False.

    Returns:
        An ipyleaflet.TileLayer or folium.TileLayer.
    """
    import rasterio

    check_package(
        "localtileserver", URL="https://github.com/banesullivan/localtileserver"
    )

    # Handle legacy localtileserver kwargs
    if "cmap" in kwargs:
        warnings.warn(
            "`cmap` is a deprecated keyword argument for get_local_tile_layer. Please use `colormap`."
        )
    if "palette" in kwargs:
        warnings.warn(
            "`palette` is a deprecated keyword argument for get_local_tile_layer. Please use `colormap`."
        )
    if "band" in kwargs or "bands" in kwargs:
        warnings.warn(
            "`band` and `bands` are deprecated keyword arguments for get_local_tile_layer. Please use `indexes`."
        )
    if "projection" in kwargs:
        warnings.warn(
            "`projection` is a deprecated keyword argument for get_local_tile_layer and will be ignored."
        )
    if "style" in kwargs:
        warnings.warn(
            "`style` is a deprecated keyword argument for get_local_tile_layer and will be ignored."
        )

    if "max_zoom" not in kwargs:
        kwargs["max_zoom"] = 30
    if "max_native_zoom" not in kwargs:
        kwargs["max_native_zoom"] = 30
    if "cmap" in kwargs:
        colormap = kwargs.pop("cmap")
    if "palette" in kwargs:
        colormap = kwargs.pop("palette")
    if "band" in kwargs:
        indexes = kwargs.pop("band")
    if "bands" in kwargs:
        indexes = kwargs.pop("bands")

    for key in client_args:
        kwargs[key] = client_args[key]

    # Make it compatible with binder and JupyterHub
    if os.environ.get("JUPYTERHUB_SERVICE_PREFIX") is not None:
        os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = (
            f"{os.environ['JUPYTERHUB_SERVICE_PREFIX'].lstrip('/')}/proxy/{{port}}"
        )

    if is_studio_lab():
        os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = (
            f"studiolab/default/jupyter/proxy/{{port}}"
        )
    elif is_on_aws():
        os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = "proxy/{port}"
    elif "prefix" in kwargs:
        os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = kwargs["prefix"]
        kwargs.pop("prefix")

    from localtileserver import (
        TileClient,
        get_folium_tile_layer,
        get_leaflet_tile_layer,
    )

    # if "show_loading" not in kwargs:
    #     kwargs["show_loading"] = False

    if isinstance(source, str):
        if not source.startswith("http"):
            if source.startswith("~"):
                source = os.path.expanduser(source)
            # else:
            #     source = os.path.abspath(source)
            # if not os.path.exists(source):
            #     raise ValueError("The source path does not exist.")
        else:
            source = github_raw_url(source)
    elif isinstance(source, TileClient) or isinstance(
        source, rasterio.io.DatasetReader
    ):
        pass

    else:
        raise ValueError("The source must either be a string or TileClient")

    if tile_format not in ["ipyleaflet", "folium"]:
        raise ValueError("The tile format must be either ipyleaflet or folium.")

    if layer_name is None:
        if source.startswith("http"):
            layer_name = "RemoteTile_" + random_string(3)
        else:
            layer_name = "LocalTile_" + random_string(3)

    if nodata is None:
        nodata = get_api_key("NODATA")
        if isinstance(nodata, str):
            nodata = float(nodata)

    if isinstance(colormap, str):
        colormap = colormap.lower()

    if quiet:
        output = widgets.Output()
        with output:
            if tile_format == "ipyleaflet":
                tile_layer = get_leaflet_tile_layer(
                    source,
                    port=port,
                    debug=debug,
                    indexes=indexes,
                    colormap=colormap,
                    vmin=vmin,
                    vmax=vmax,
                    nodata=nodata,
                    attribution=attribution,
                    name=layer_name,
                    **kwargs,
                )
            else:
                tile_layer = get_folium_tile_layer(
                    source,
                    port=port,
                    debug=debug,
                    indexes=indexes,
                    colormap=colormap,
                    vmin=vmin,
                    vmax=vmax,
                    nodata=nodata,
                    attr=attribution,
                    overlay=True,
                    name=layer_name,
                    **kwargs,
                )
    else:
        if tile_format == "ipyleaflet":
            tile_layer = get_leaflet_tile_layer(
                source,
                port=port,
                debug=debug,
                indexes=indexes,
                colormap=colormap,
                vmin=vmin,
                vmax=vmax,
                nodata=nodata,
                attribution=attribution,
                name=layer_name,
                **kwargs,
            )
        else:
            tile_layer = get_folium_tile_layer(
                source,
                port=port,
                debug=debug,
                indexes=indexes,
                colormap=colormap,
                vmin=vmin,
                vmax=vmax,
                nodata=nodata,
                attr=attribution,
                overlay=True,
                name=layer_name,
                **kwargs,
            )

    if return_client:
        return tile_layer, tile_layer.tile_server
    else:
        return tile_layer


def get_local_tile_url(
    source,
    port="default",
    indexes=None,
    colormap=None,
    vmin=None,
    vmax=None,
    nodata=None,
    client_args={"cors_all": False},
    return_client=False,
    **kwargs: Any,
):
    """Generate an ipyleaflet/folium TileLayer from a local raster dataset or remote Cloud Optimized GeoTIFF (COG).
        If you are using this function in JupyterHub on a remote server and the raster does not render properly, try
        running the following two lines before calling this function:

        import os
        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = 'proxy/{port}'

    Args:
        source (str): The path to the GeoTIFF file or the URL of the Cloud Optimized GeoTIFF.
        port (str, optional): The port to use for the server. Defaults to "default".
        indexes (int, optional): The band(s) to use. Band indexing starts at 1. Defaults to None.
        colormap (str, optional): The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.
        vmin (float, optional): The minimum value to use when colormapping the colormap when plotting a single band. Defaults to None.
        vmax (float, optional): The maximum value to use when colormapping the colormap when plotting a single band. Defaults to None.
        nodata (float, optional): The value from the band to use to interpret as not valid data. Defaults to None.
        client_args (dict, optional): Additional arguments to pass to the TileClient. Defaults to {}.
        return_client (bool, optional): If True, the tile client will be returned. Defaults to False.

    Returns:
        An ipyleaflet.TileLayer or folium.TileLayer.
    """
    import rasterio

    check_package(
        "localtileserver", URL="https://github.com/banesullivan/localtileserver"
    )

    # Handle legacy localtileserver kwargs
    if "cmap" in kwargs:
        warnings.warn(
            "`cmap` is a deprecated keyword argument for get_local_tile_layer. Please use `colormap`."
        )
    if "palette" in kwargs:
        warnings.warn(
            "`palette` is a deprecated keyword argument for get_local_tile_layer. Please use `colormap`."
        )
    if "band" in kwargs or "bands" in kwargs:
        warnings.warn(
            "`band` and `bands` are deprecated keyword arguments for get_local_tile_layer. Please use `indexes`."
        )
    if "projection" in kwargs:
        warnings.warn(
            "`projection` is a deprecated keyword argument for get_local_tile_layer and will be ignored."
        )
    if "style" in kwargs:
        warnings.warn(
            "`style` is a deprecated keyword argument for get_local_tile_layer and will be ignored."
        )

    if "max_zoom" not in kwargs:
        kwargs["max_zoom"] = 30
    if "max_native_zoom" not in kwargs:
        kwargs["max_native_zoom"] = 30
    if "cmap" in kwargs:
        colormap = kwargs.pop("cmap")
    if "palette" in kwargs:
        colormap = kwargs.pop("palette")
    if "band" in kwargs:
        indexes = kwargs.pop("band")
    if "bands" in kwargs:
        indexes = kwargs.pop("bands")

    for key in client_args:
        kwargs[key] = client_args[key]

    # Make it compatible with binder and JupyterHub
    if os.environ.get("JUPYTERHUB_SERVICE_PREFIX") is not None:
        os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = (
            f"{os.environ['JUPYTERHUB_SERVICE_PREFIX'].lstrip('/')}/proxy/{{port}}"
        )

    if is_studio_lab():
        os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = (
            f"studiolab/default/jupyter/proxy/{{port}}"
        )
    elif is_on_aws():
        os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = "proxy/{port}"
    elif "prefix" in kwargs:
        os.environ["LOCALTILESERVER_CLIENT_PREFIX"] = kwargs["prefix"]
        kwargs.pop("prefix")

    from localtileserver import TileClient

    # if "show_loading" not in kwargs:
    #     kwargs["show_loading"] = False

    if isinstance(source, str):
        if not source.startswith("http"):
            if source.startswith("~"):
                source = os.path.expanduser(source)

        else:
            source = github_raw_url(source)
    elif isinstance(source, TileClient) or isinstance(
        source, rasterio.io.DatasetReader
    ):
        pass

    else:
        raise ValueError("The source must either be a string or TileClient")

    if nodata is None:
        nodata = get_api_key("NODATA")
        if isinstance(nodata, str):
            nodata = float(nodata)

    if isinstance(colormap, str):
        colormap = colormap.lower()

    client = TileClient(source, port=port, **client_args)
    url = client.get_tile_url(
        indexes=indexes,
        colormap=colormap,
        vmin=vmin,
        vmax=vmax,
        nodata=nodata,
    )

    if return_client:
        return url, client
    else:
        return url


def get_palettable(types=None):
    """Get a list of palettable color palettes.

    Args:
        types (list, optional): A list of palettable types to return, e.g., types=['matplotlib', 'cartocolors']. Defaults to None.

    Returns:
        A list of palettable color palettes.
    """
    try:
        import palettable
    except ImportError:
        raise ImportError(
            "Please install the palettable package using 'pip install palettable'."
        )

    if types is not None and (not isinstance(types, list)):
        raise ValueError("The types must be a list.")

    allowed_palettes = [
        "cartocolors",
        "cmocean",
        "colorbrewer",
        "cubehelix",
        "lightbartlein",
        "matplotlib",
        "mycarta",
        "scientific",
        "tableau",
        "wesanderson",
    ]

    if types is None:
        types = allowed_palettes[:]

    if all(x in allowed_palettes for x in types):
        pass
    else:
        raise ValueError(
            "The types must be one of the following: " + ", ".join(allowed_palettes)
        )

    palettes = []

    if "cartocolors" in types:
        cartocolors_diverging = [
            f"cartocolors.diverging.{c}"
            for c in dir(palettable.cartocolors.diverging)[:-19]
        ]
        cartocolors_qualitative = [
            f"cartocolors.qualitative.{c}"
            for c in dir(palettable.cartocolors.qualitative)[:-19]
        ]
        cartocolors_sequential = [
            f"cartocolors.sequential.{c}"
            for c in dir(palettable.cartocolors.sequential)[:-41]
        ]

        palettes = (
            palettes
            + cartocolors_diverging
            + cartocolors_qualitative
            + cartocolors_sequential
        )

    if "cmocean" in types:
        cmocean_diverging = [
            f"cmocean.diverging.{c}" for c in dir(palettable.cmocean.diverging)[:-19]
        ]
        cmocean_sequential = [
            f"cmocean.sequential.{c}" for c in dir(palettable.cmocean.sequential)[:-19]
        ]

        palettes = palettes + cmocean_diverging + cmocean_sequential

    if "colorbrewer" in types:
        colorbrewer_diverging = [
            f"colorbrewer.diverging.{c}"
            for c in dir(palettable.colorbrewer.diverging)[:-19]
        ]
        colorbrewer_qualitative = [
            f"colorbrewer.qualitative.{c}"
            for c in dir(palettable.colorbrewer.qualitative)[:-19]
        ]
        colorbrewer_sequential = [
            f"colorbrewer.sequential.{c}"
            for c in dir(palettable.colorbrewer.sequential)[:-41]
        ]

        palettes = (
            palettes
            + colorbrewer_diverging
            + colorbrewer_qualitative
            + colorbrewer_sequential
        )

    if "cubehelix" in types:
        cubehelix = [
            "classic_16",
            "cubehelix1_16",
            "cubehelix2_16",
            "cubehelix3_16",
            "jim_special_16",
            "perceptual_rainbow_16",
            "purple_16",
            "red_16",
        ]
        cubehelix = [f"cubehelix.{c}" for c in cubehelix]
        palettes = palettes + cubehelix

    if "lightbartlein" in types:
        lightbartlein_diverging = [
            f"lightbartlein.diverging.{c}"
            for c in dir(palettable.lightbartlein.diverging)[:-19]
        ]
        lightbartlein_sequential = [
            f"lightbartlein.sequential.{c}"
            for c in dir(palettable.lightbartlein.sequential)[:-19]
        ]

        palettes = palettes + lightbartlein_diverging + lightbartlein_sequential

    if "matplotlib" in types:
        matplotlib_colors = [
            f"matplotlib.{c}" for c in dir(palettable.matplotlib)[:-16]
        ]
        palettes = palettes + matplotlib_colors

    if "mycarta" in types:
        mycarta = [f"mycarta.{c}" for c in dir(palettable.mycarta)[:-16]]
        palettes = palettes + mycarta

    if "scientific" in types:
        scientific_diverging = [
            f"scientific.diverging.{c}"
            for c in dir(palettable.scientific.diverging)[:-19]
        ]
        scientific_sequential = [
            f"scientific.sequential.{c}"
            for c in dir(palettable.scientific.sequential)[:-19]
        ]

        palettes = palettes + scientific_diverging + scientific_sequential

    if "tableau" in types:
        tableau = [f"tableau.{c}" for c in dir(palettable.tableau)[:-14]]
        palettes = palettes + tableau

    return palettes


def points_from_xy(data, x=None, y=None, z=None, crs=None, **kwargs: Any):
    """Create a GeoPandas GeoDataFrame from a csv or Pandas DataFrame containing x, y, z values.

    Args:
        data (str | pd.DataFrame): A csv or Pandas DataFrame containing x, y, z values.
        x (str, optional): The column name for the x values. Defaults to "longitude".
        y (str, optional): The column name for the y values. Defaults to "latitude".
        z (str, optional): The column name for the z values. Defaults to None.
        crs (str | int, optional): The coordinate reference system for the GeoDataFrame. Defaults to None.

    Returns:
        A GeoPandas GeoDataFrame containing x, y, z values.
    """
    check_package(name="geopandas", URL="https://geopandas.org")
    import geopandas as gpd
    import pandas as pd

    if crs is None:
        crs = "epsg:4326"

    if isinstance(data, pd.DataFrame):
        df = data
    elif isinstance(data, str):
        if not data.startswith("http") and (not os.path.exists(data)):
            raise FileNotFoundError("The specified input csv does not exist.")
        else:
            df = pd.read_csv(data, **kwargs)
    else:
        raise TypeError("The data must be a pandas DataFrame or a csv file path.")

    columns = df.columns

    if x is None:
        if "longitude" in columns:
            x = "longitude"
        elif "x" in columns:
            x = "x"
        elif "lon" in columns:
            x = "lon"
        else:
            raise ValueError("The x column could not be found.")

    if y is None:
        if "latitude" in columns:
            y = "latitude"
        elif "y" in columns:
            y = "y"
        elif "lat" in columns:
            y = "lat"
        else:
            raise ValueError("The y column could not be found.")

    gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df[x], df[y], z=z, crs=crs))

    return gdf


def html_to_streamlit(
    html,
    width=800,
    height=600,
    responsive=True,
    scrolling=False,
    token_name=None,
    token_value=None,
    **kwargs: Any,
):
    """Renders an HTML file in a Streamlit app. This method is a static Streamlit Component, meaning, no information is passed back from Leaflet on browser interaction.

    Args:
        html (str): The HTML file to render. It can a local file path or a URL.
        width (int, optional): Width of the map. Defaults to 800.
        height (int, optional): Height of the map. Defaults to 600.
        responsive (bool, optional): Whether to make the map responsive. Defaults to True.
        scrolling (bool, optional): Whether to allow the map to scroll. Defaults to False.
        token_name (str, optional): The name of the token in the HTML file to be replaced. Defaults to None.
        token_value (str, optional): The value of the token to pass to the HTML file. Defaults to None.

    Returns:
        A Streamlit components.html object.
    """

    try:
        import streamlit as st  # pylint: disable=E0401
        import streamlit.components.v1 as components  # pylint: disable=E0401

        if isinstance(html, str):
            temp_path = None
            if html.startswith("http") and html.endswith(".html"):
                temp_path = temp_file_path(".html")
                out_file = os.path.basename(temp_path)
                out_dir = os.path.dirname(temp_path)
                download_from_url(html, out_file, out_dir)
                html = temp_path

            elif not os.path.exists(html):
                raise FileNotFoundError("The specified input html does not exist.")

            with open(html) as f:
                lines = f.readlines()
                if (token_name is not None) and (token_value is not None):
                    lines = [line.replace(token_name, token_value) for line in lines]
                html_str = "".join(lines)

            if temp_path is not None:
                os.remove(temp_path)

            if responsive:
                make_map_responsive = """
                <style>
                [title~="st.iframe"] { width: 100%}
                </style>
                """
                st.markdown(make_map_responsive, unsafe_allow_html=True)
            return components.html(
                html_str, width=width, height=height, scrolling=scrolling
            )
        else:
            raise TypeError("The html must be a string.")

    except Exception as e:
        raise Exception(e)


def cesium_to_streamlit(
    html,
    width=800,
    height=600,
    responsive=True,
    scrolling=False,
    token_name=None,
    token_value=None,
    **kwargs: Any,
) -> Any:
    """Renders an cesium HTML file in a Streamlit app. This method is a static Streamlit Component, meaning, no information is passed back from Leaflet on browser interaction.

    Args:
        html (str): The HTML file to render. It can a local file path or a URL.
        width (int, optional): Width of the map. Defaults to 800.
        height (int, optional): Height of the map. Defaults to 600.
        responsive (bool, optional): Whether to make the map responsive. Defaults to True.
        scrolling (bool, optional): Whether to allow the map to scroll. Defaults to False.
        token_name (str, optional): The name of the token in the HTML file to be replaced. Defaults to None.
        token_value (str, optional): The value of the token to pass to the HTML file. Defaults to None.

    Returns:
        A Streamlit components.html object.
    """
    if token_name is None:
        token_name = "your_access_token"

    if token_value is None:
        token_value = os.environ.get("CESIUM_TOKEN")

    html_to_streamlit(
        html, width, height, responsive, scrolling, token_name, token_value
    )


def geom_type(in_geojson, encoding="utf-8"):
    """Returns the geometry type of a GeoJSON object.

    Args:
        in_geojson (dict): A GeoJSON object.
        encoding (str, optional): The encoding of the GeoJSON object. Defaults to "utf-8".

    Returns:
        The geometry type of the GeoJSON object, such as Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon.
            For more info, see https://shapely.readthedocs.io/en/stable/manual.html
    """
    import json

    try:
        if isinstance(in_geojson, str):
            if in_geojson.startswith("http"):
                data = requests.get(in_geojson).json()
            else:
                in_geojson = os.path.abspath(in_geojson)
                if not os.path.exists(in_geojson):
                    raise FileNotFoundError(
                        "The provided GeoJSON file could not be found."
                    )

                with open(in_geojson, encoding=encoding) as f:
                    data = json.load(f)
        elif isinstance(in_geojson, dict):
            data = in_geojson
        else:
            raise TypeError("The input geojson must be a type of str or dict.")

        return data["features"][0]["geometry"]["type"]

    except Exception as e:
        raise Exception(e)


def geojson_to_gdf(in_geojson, encoding="utf-8", **kwargs: Any):
    """Converts a GeoJSON object to a geopandas GeoDataFrame.

    Args:
        in_geojson (str | dict): The input GeoJSON file or GeoJSON object as a dict.
        encoding (str, optional): The encoding of the GeoJSON object. Defaults to "utf-8".

    Returns:
        A GeoPandas GeoDataFrame containing the GeoJSON object.
    """

    import geopandas as gpd

    if isinstance(in_geojson, dict):
        out_file = temp_file_path(extension="geojson")
        with open(out_file, "w") as f:
            json.dump(in_geojson, f)
            in_geojson = out_file

    gdf = gpd.read_file(in_geojson, encoding=encoding, **kwargs)
    return gdf


def geojson_to_df(in_geojson, encoding="utf-8", drop_geometry=True):
    """Converts a GeoJSON object to a pandas DataFrame.

    Args:
        in_geojson (str | dict): The input GeoJSON file or dict.
        encoding (str, optional): The encoding of the GeoJSON object. Defaults to "utf-8".
        drop_geometry (bool, optional): Whether to drop the geometry column. Defaults to True.

    Raises:
        FileNotFoundError: If the input GeoJSON file could not be found.

    Returns:
        A pandas DataFrame containing the GeoJSON object.
    """

    import json
    from urllib.request import urlopen

    import pandas as pd

    if isinstance(in_geojson, str):
        if in_geojson.startswith("http"):
            with urlopen(in_geojson) as f:
                data = json.load(f)
        else:
            in_geojson = os.path.abspath(in_geojson)
            if not os.path.exists(in_geojson):
                raise FileNotFoundError("The provided GeoJSON file could not be found.")

            with open(in_geojson, encoding=encoding) as f:
                data = json.load(f)

    elif isinstance(in_geojson, dict):
        data = in_geojson

    df = pd.json_normalize(data["features"])
    df.columns = [col.replace("properties.", "") for col in df.columns]
    if drop_geometry:
        df = df[df.columns.drop(list(df.filter(regex="geometry")))]
    return df


def geojson_to_shp(in_geojson, out_shp, **kwargs: Any):
    """Converts a GeoJSON object to GeoPandas GeoDataFrame.

    Args:
        in_geojson (str | dict): The input GeoJSON file or dict.
        out_shp (str): The output shapefile path.
    """
    import json

    import geopandas as gpd

    ext = os.path.splitext(out_shp)[1]
    if ext != ".shp":
        out_shp = out_shp + ".shp"
    out_shp = check_file_path(out_shp)

    if isinstance(in_geojson, dict):
        out_file = temp_file_path(extension="geojson")
        with open(out_file, "w") as f:
            json.dump(in_geojson, f)
            in_geojson = out_file

    gdf = gpd.read_file(in_geojson, **kwargs)
    gdf.to_file(out_shp)


def geojson_to_gpkg(in_geojson, out_gpkg, **kwargs: Any):
    """Converts a GeoJSON object to GeoPackage.

    Args:
        in_geojson (str | dict): The input GeoJSON file or dict.
        out_gpkg (str): The output GeoPackage path.
    """
    import json

    import geopandas as gpd

    ext = os.path.splitext(out_gpkg)[1]
    if ext.lower() != ".gpkg":
        out_gpkg = out_gpkg + ".gpkg"
    out_gpkg = check_file_path(out_gpkg)

    if isinstance(in_geojson, dict):
        out_file = temp_file_path(extension="geojson")
        with open(out_file, "w") as f:
            json.dump(in_geojson, f)
            in_geojson = out_file

    gdf = gpd.read_file(in_geojson, **kwargs)
    name = os.path.splitext(os.path.basename(out_gpkg))[0]
    gdf.to_file(out_gpkg, layer=name, driver="GPKG")


def gdf_to_df(gdf, drop_geom=True):
    """Converts a GeoDataFrame to a pandas DataFrame.

    Args:
        gdf (gpd.GeoDataFrame): A GeoDataFrame.
        drop_geom (bool, optional): Whether to drop the geometry column. Defaults to True.

    Returns:
        A pandas DataFrame containing the GeoDataFrame.
    """
    import pandas as pd

    if drop_geom:
        df = pd.DataFrame(gdf.drop(columns=["geometry"]))
    else:
        df = pd.DataFrame(gdf)

    return df


def gdf_bounds(gdf, return_geom=False):
    """Returns the bounding box of a GeoDataFrame.

    Args:
        gdf (gpd.GeoDataFrame): A GeoDataFrame.
        return_geom (bool, optional): Whether to return the bounding box as a GeoDataFrame. Defaults to False.

    Returns:
        A bounding box in the form of a list (minx, miny, maxx, maxy) or GeoDataFrame.
    """
    bounds = gdf.total_bounds
    if return_geom:
        return bbox_to_gdf(bbox=bounds)
    else:
        return bounds


def gdf_centroid(gdf, return_geom=False):
    """Returns the centroid of a GeoDataFrame.

    Args:
        gdf (gpd.GeoDataFrame): A GeoDataFrame.
        return_geom (bool, optional): Whether to return the bounding box as a GeoDataFrame. Defaults to False.

    Returns:
        A bounding box in the form of a list (lon, lat) or GeoDataFrame.
    """

    warnings.filterwarnings("ignore")

    centroid = gdf_bounds(gdf, return_geom=True).centroid
    if return_geom:
        return centroid
    else:
        return centroid.x[0], centroid.y[0]


def gdf_geom_type(gdf, first_only=True):
    """Returns the geometry type of a GeoDataFrame.

    Args:
        gdf (gpd.GeoDataFrame): A GeoDataFrame.
        first_only (bool, optional): Whether to return the geometry type of the f
            irst feature in the GeoDataFrame. Defaults to True.

    Returns:
        The geometry type of the GeoDataFrame, such as Point, LineString,
            Polygon, MultiPoint, MultiLineString, MultiPolygon.
            For more info, see https://shapely.readthedocs.io/en/stable/manual.html
    """
    import geopandas as gpd

    if first_only:
        return gdf.geometry.type[0]
    else:
        return gdf.geometry.type


def vector_geom_type(data, first_only=True, **kwargs: Any):
    """Returns the geometry type of a vector dataset.

    Args:
        data (gpd.GeoDataFrame): A GeoDataFrame.
        first_only (bool, optional): Whether to return the geometry type of the
            first feature in the GeoDataFrame. Defaults to True.
        kwargs: Additional keyword arguments to pass to the geopandas.read_file function.


    Returns:
        The geometry type of the GeoDataFrame, such as Point, LineString,
            Polygon, MultiPoint, MultiLineString, MultiPolygon.
            For more info, see https://shapely.readthedocs.io/en/stable/manual.html
    """
    import geopandas as gpd

    if isinstance(data, str) or isinstance(data, dict):
        gdf = gpd.read_file(data, **kwargs)

    if first_only:
        return gdf.geometry.type[0]
    else:
        return gdf.geometry.type


def check_dir(dir_path, make_dirs=True) -> str:
    """Checks if a directory exists and creates it if it does not.

    Args:
        dir_path ([str): The path to the directory.
        make_dirs (bool, optional): Whether to create the directory if it does not exist. Defaults to True.

    Raises:
        FileNotFoundError: If the directory could not be found.
        TypeError: If the input directory path is not a string.

    Returns:
        The path to the directory.
    """

    if isinstance(dir_path, str):
        if dir_path.startswith("~"):
            dir_path = os.path.expanduser(dir_path)
        else:
            dir_path = os.path.abspath(dir_path)

        if not os.path.exists(dir_path) and make_dirs:
            os.makedirs(dir_path)

        if os.path.exists(dir_path):
            return dir_path
        else:
            raise FileNotFoundError("The provided directory could not be found.")
    else:
        raise TypeError("The provided directory path must be a string.")


def check_file_path(file_path, make_dirs=True) -> str:
    """Gets the absolute file path.

    Args:
        file_path (str): The path to the file.
        make_dirs (bool, optional): Whether to create the directory if it does not exist. Defaults to True.

    Raises:
        FileNotFoundError: If the directory could not be found.
        TypeError: If the input directory path is not a string.

    Returns:
        The absolute path to the file.
    """
    if isinstance(file_path, str):
        if file_path.startswith("~"):
            file_path = os.path.expanduser(file_path)
        else:
            file_path = os.path.abspath(file_path)

        file_dir = os.path.dirname(file_path)
        if not os.path.exists(file_dir) and make_dirs:
            os.makedirs(file_dir)

        return file_path

    else:
        raise TypeError("The provided file path must be a string.")


def dict_to_json(data, file_path, indent=4):
    """Writes a dictionary to a JSON file.

    Args:
        data (dict): A dictionary.
        file_path (str): The path to the JSON file.
        indent (int, optional): The indentation of the JSON file. Defaults to 4.

    Raises:
        TypeError: If the input data is not a dictionary.
    """
    import json

    file_path = check_file_path(file_path)

    if isinstance(data, dict):
        with open(file_path, "w") as f:
            json.dump(data, f, indent=indent)
    else:
        raise TypeError("The provided data must be a dictionary.")


def image_to_geotiff(image, dst_path, dtype=None, to_cog=True, **kwargs) -> None:
    """
    Converts an image to a GeoTIFF file.

    This function takes an image in the form of a rasterio.io.DatasetReader object, and writes it to a GeoTIFF file
    at the specified destination path. The data type of the output GeoTIFF can be specified. Additional keyword
    arguments can be passed to customize the GeoTIFF profile.

    Args:
        image (DatasetReader): The input image as a rasterio.io.DatasetReader object.
        dst_path (str): The destination path where the GeoTIFF file will be saved.
        dtype (Optional[str]): The data type for the output GeoTIFF file. If None, the data type of the input image
            will be used. Defaults to None.
        to_cog (bool): Whether to convert the output GeoTIFF to a Cloud Optimized GeoTIFF (COG). Defaults to True.
        **kwargs: Additional keyword arguments to be included in the GeoTIFF profile.

    Raises:
        ValueError: If the input image is not a rasterio.io.DatasetReader object.

    Returns:
        None
    """
    import rasterio
    from rasterio.enums import Resampling

    if not isinstance(image, rasterio.io.DatasetReader):
        raise ValueError("The input image must be a rasterio.io.DatasetReader object.")

    dst_path = check_file_path(dst_path)

    profile = image.profile
    if dtype is not None:
        profile["dtype"] = dtype

    for key, value in kwargs.items():
        profile[key] = value

    with rasterio.open(dst_path, "w", **profile) as dst:
        dst.write(image.read())

    if to_cog:
        image_to_cog(dst_path, dst_path)


def image_to_cog(source, dst_path=None, profile="deflate", BIGTIFF=None, **kwargs: Any):
    """Converts an image to a COG file.

    Args:
        source (str): A dataset path, URL or rasterio.io.DatasetReader object.
        dst_path (str, optional): An output dataset path or or PathLike object. Defaults to None.
        profile (str, optional): COG profile. More at https://cogeotiff.github.io/rio-cogeo/profile. Defaults to "deflate".
        BIGTIFF (str, optional): Create a BigTIFF file. Can be "IF_SAFER" or "YES". Defaults to None.

    Raises:
        ImportError: If rio-cogeo is not installed.
        FileNotFoundError: If the source file could not be found.
    """
    try:
        from rio_cogeo.cogeo import cog_translate
        from rio_cogeo.profiles import cog_profiles

    except ImportError:
        raise ImportError(
            "The rio-cogeo package is not installed. Please install it with `pip install rio-cogeo` or `conda install rio-cogeo -c conda-forge`."
        )

    if not source.startswith("http"):
        source = check_file_path(source)

        if not os.path.exists(source):
            raise FileNotFoundError("The provided input file could not be found.")

    if dst_path is None:
        if not source.startswith("http"):
            dst_path = os.path.splitext(source)[0] + "_cog.tif"
        else:
            dst_path = temp_file_path(extension=".tif")

    dst_path = check_file_path(dst_path)

    dst_profile = cog_profiles.get(profile)
    if "dst_kwargs" in kwargs:
        dst_profile.update(kwargs.pop("dst_kwargs"))

    if BIGTIFF is not None:
        dst_profile.update({"BIGTIFF": BIGTIFF})
    cog_translate(source, dst_path, dst_profile, **kwargs)


def cog_validate(source, verbose=False) -> Tuple[bool, List[str], List[str]]:
    """Validate Cloud Optimized Geotiff.

    Args:
        source (str): A dataset path or URL. Will be opened in "r" mode.
        verbose (bool, optional): Whether to print the output of the validation. Defaults to False.

    Raises:
        ImportError: If the rio-cogeo package is not installed.
        FileNotFoundError: If the provided file could not be found.

    Returns:
        A tuple containing the validation results (True if src_path is a valid COG, list of validation errors, and a list of validation warnings).
    """
    try:
        from rio_cogeo.cogeo import cog_info, cog_validate
    except ImportError:
        raise ImportError(
            "The rio-cogeo package is not installed. Please install it with `pip install rio-cogeo` or `conda install rio-cogeo -c conda-forge`."
        )

    if not source.startswith("http"):
        source = check_file_path(source)

        if not os.path.exists(source):
            raise FileNotFoundError("The provided input file could not be found.")

    if verbose:
        return cog_info(source)
    else:
        return cog_validate(source)


def image_to_numpy(image):
    """Converts an image to a numpy array.

    Args:
        image (str): A dataset path, URL or rasterio.io.DatasetReader object.

    Raises:
        FileNotFoundError: If the provided file could not be found.

    Returns:
        A numpy array.
    """
    import rasterio
    from osgeo import gdal

    # ... and suppress errors
    gdal.PushErrorHandler("CPLQuietErrorHandler")

    try:
        with rasterio.open(image, "r") as ds:
            arr = ds.read()  # read all raster values
        return arr
    except Exception as e:
        raise Exception(e)


def numpy_to_image(
    np_array,
    filename: str,
    transpose: bool = True,
    bands: Union[int, list] = None,
    size: Tuple = None,
    resize_args: dict = None,
    **kwargs: Any,
) -> None:
    """Converts a numpy array to an image in the specified format, such as JPG, PNG, TIFF, etc.

    Args:
        np_array (np.ndarray): A numpy array or a path to a raster file.
        filename (str): The output filename.
        transpose (bool, optional): Whether to transpose the array from (bands, rows, cols) to (rows, cols, bands). Defaults to True.
        bands (int | list, optional): The band(s) to use, starting from 0. Defaults to None.

    """

    import numpy as np
    from PIL import Image

    warnings.filterwarnings("ignore")

    if isinstance(np_array, str):
        np_array = image_to_numpy(np_array)

    if not isinstance(np_array, np.ndarray):
        raise TypeError("The provided input must be a numpy array.")

    if np_array.dtype == np.float64 or np_array.dtype == np.float32:
        # Convert the array to uint8
        # np_array = (np_array * 255).astype(np.uint8)
        np.interp(np_array, (np_array.min(), np_array.max()), (0, 255)).astype(np.uint8)
    else:
        # The array is already uint8
        np_array = np_array

    if np_array.ndim == 2:
        img = Image.fromarray(np_array)
    elif np_array.ndim == 3:
        if transpose:
            np_array = np_array.transpose(1, 2, 0)
        if bands is None:
            if np_array.shape[2] < 3:
                np_array = np_array[:, :, 0]
            elif np_array.shape[2] > 3:
                np_array = np_array[:, :, :3]

        elif isinstance(bands, list):
            if len(bands) == 1:
                np_array = np_array[:, :, bands[0]]
            else:
                np_array = np_array[:, :, bands]
        elif isinstance(bands, int):
            np_array = np_array[:, :, bands]
        img = Image.fromarray(np_array)
    else:
        raise ValueError("The provided input must be a 2D or 3D numpy array.")

    if isinstance(size, tuple):
        try:
            from skimage.transform import resize
        except ImportError:
            raise ImportError(
                "The scikit-image package is not installed. Please install it with `pip install scikit-image` \
                  or `conda install scikit-image -c conda-forge`."
            )
        if resize_args is None:
            resize_args = {}
        if "preserve_range" not in resize_args:
            resize_args["preserve_range"] = True
        np_array = resize(np_array, size, **resize_args).astype("uint8")
        img = Image.fromarray(np_array)

    img.save(filename, **kwargs)


def numpy_to_cog(
    np_array,
    out_cog,
    bounds=None,
    profile=None,
    dtype=None,
    dst_crs=None,
    coord_crs=None,
):
    """Converts a numpy array to a COG file.

    Args:
        np_array (np.array): A numpy array representing an image or an HTTP URL to an image.
        out_cog (str): The output COG file path.
        bounds (tuple, optional): The bounds of the image in the format of (minx, miny, maxx, maxy). Defaults to None.
        profile (str | dict, optional): File path to an existing COG file or a dictionary representing the profile. Defaults to None.
        dtype (str, optional): The data type of the output COG file. Defaults to None.
        dst_crs (str, optional): The coordinate reference system of the output COG file. Defaults to "epsg:4326".
        coord_crs (str, optional): The coordinate reference system of bbox coordinates. Defaults to None.

    """

    import numpy as np
    import rasterio
    from rasterio.io import MemoryFile
    from rasterio.transform import from_bounds
    from rio_cogeo.cogeo import cog_translate
    from rio_cogeo.profiles import cog_profiles

    warnings.filterwarnings("ignore")

    if isinstance(np_array, str):
        with rasterio.open(np_array, "r") as ds:
            np_array = ds.read()

    if not isinstance(np_array, np.ndarray):
        raise TypeError("The input array must be a numpy array.")

    out_dir = os.path.dirname(out_cog)
    check_dir(out_dir)

    if profile is not None:
        if isinstance(profile, str):
            if (not profile.startswith("http")) and (not os.path.exists(profile)):
                raise FileNotFoundError("The provided file could not be found.")
            with rasterio.open(profile) as ds:
                dst_crs = ds.crs
                if bounds is None:
                    bounds = ds.bounds

        elif isinstance(profile, rasterio.profiles.Profile):
            profile = dict(profile)
        elif not isinstance(profile, dict):
            raise TypeError("The provided profile must be a file path or a dictionary.")

    if bounds is None:
        print(
            "warning: bounds is not set. Using the default bounds (-180.0, -85.0511, 180.0, 85.0511)"
        )
        bounds = (-180.0, -85.0511287798066, 180.0, 85.0511287798066)

    if not isinstance(bounds, tuple) and len(bounds) != 4:
        raise TypeError("The provided bounds must be a tuple of length 4.")

    # Rasterio uses numpy array of shape of `(bands, height, width)`

    if len(np_array.shape) == 3:
        nbands = np_array.shape[0]
        height = np_array.shape[1]
        width = np_array.shape[2]
    elif len(np_array.shape) == 2:
        nbands = 1
        height = np_array.shape[0]
        width = np_array.shape[1]
        np_array = np_array.reshape((1, height, width))
    else:
        raise ValueError("The input array must be a 2D or 3D numpy array.")

    if coord_crs is not None and dst_crs is not None:
        bounds = transform_bbox_coords(bounds, coord_crs, dst_crs)

    src_transform = from_bounds(*bounds, width=width, height=height)
    if dtype is None:
        dtype = str(np_array.dtype)

    if dst_crs is None:
        dst_crs = "epsg:4326"

    if isinstance(profile, dict):
        src_profile = profile
        src_profile["count"] = nbands
    else:
        src_profile = dict(
            driver="GTiff",
            dtype=dtype,
            count=nbands,
            height=height,
            width=width,
            crs=dst_crs,
            transform=src_transform,
        )

    with MemoryFile() as memfile:
        with memfile.open(**src_profile) as mem:
            # Populate the input file with numpy array
            mem.write(np_array)

            dst_profile = cog_profiles.get("deflate")
            cog_translate(
                mem,
                out_cog,
                dst_profile,
                in_memory=True,
                quiet=True,
            )


def get_stac_collections(url, **kwargs: Any):
    """Retrieve a list of STAC collections from a URL.
    This function is adapted from https://github.com/mykolakozyr/stacdiscovery/blob/a5d1029aec9c428a7ce7ae615621ea8915162824/app.py#L31.
    Credits to Mykola Kozyr.

    Args:
        url (str): A URL to a STAC catalog.
        **kwargs: Additional keyword arguments to pass to the pystac Client.open() method.
            See https://pystac-client.readthedocs.io/en/stable/api.html#pystac_client.Client.open

    Returns:
        A list of STAC collections.
    """
    from pystac_client import Client

    # Expensive function. Added cache for it.
    # Empty list that would be used for a dataframe to collect and visualize info about collections
    root_catalog = Client.open(url, **kwargs)
    collections_list = []
    # Reading collections in the Catalog
    collections = list(root_catalog.get_collections())
    print(collections)
    for collection in collections:
        id = collection.id
        title = collection.title
        # bbox = collection.extent.spatial.bboxes # not in use for the first release
        # interval = collection.extent.temporal.intervals # not in use for the first release
        description = collection.description

        # creating a list of lists of values
        collections_list.append([id, title, description])
    return collections_list


def get_stac_items(
    url,
    collection,
    limit=None,
    bbox=None,
    datetime=None,
    intersects=None,
    ids=None,
    open_args=None,
    **kwargs: Any,
):
    """Retrieve a list of STAC items from a URL and a collection.
    This function is adapted from https://github.com/mykolakozyr/stacdiscovery/blob/a5d1029aec9c428a7ce7ae615621ea8915162824/app.py#L49.
    Credits to Mykola Kozyr.
    Available parameters can be found at https://github.com/radiantearth/stac-api-spec/tree/master/item-search

    Args:
        url (str): A URL to a STAC catalog.
        collection (str): A STAC collection ID.
        limit (int, optional): The maximum number of results to return (page size). Defaults to None.
        bbox (tuple, optional): Requested bounding box in the format of (minx, miny, maxx, maxy). Defaults to None.
        datetime (str, optional): Single date+time, or a range ('/' separator), formatted to RFC 3339, section 5.6. Use double dots .. for open date ranges.
        intersects (dict, optional): A dictionary representing a GeoJSON Geometry. Searches items by performing intersection between their geometry and provided GeoJSON geometry. All GeoJSON geometry types must be supported.
        ids (list, optional): A list of item ids to return.
        open_args (dict, optional): A dictionary of arguments to pass to the pystac Client.open() method. Defaults to None.
        **kwargs: Additional keyword arguments to pass to the Catalog.search() method.

    Returns:
        A GeoDataFrame with the STAC items.
    """

    import itertools

    import geopandas as gpd
    from pystac_client import Client
    from shapely.geometry import shape

    # Empty list that would be used for a dataframe to collect and visualize info about collections
    items_list = []

    if open_args is None:
        open_args = {}

    root_catalog = Client.open(url)

    if limit:
        kwargs["limit"] = limit
    if bbox:
        kwargs["bbox"] = bbox
    if datetime:
        kwargs["datetime"] = datetime
    if intersects:
        kwargs["intersects"] = intersects
    if ids:
        kwargs["ids"] = ids

    if kwargs:
        try:
            catalog = root_catalog.search(collections=collection, **kwargs)
        except NotImplementedError:
            catalog = root_catalog
    else:
        catalog = root_catalog

    iterable = catalog.get_all_items()
    items = list(
        itertools.islice(iterable, limit)
    )  # getting first 25000 items. To Do some smarter logic
    if len(items) == 0:
        try:
            catalog = root_catalog.get_child(collection)
            iterable = catalog.get_all_items()
            items = list(itertools.islice(iterable, limit))
        except Exception as _:
            print("Ooops, it looks like this collection does not have items.")
            return None
    # Iterating over items to collect main information
    for item in items:
        id = item.id
        geometry = shape(item.geometry)
        datetime = (
            item.datetime
            or item.properties["datetime"]
            or item.properties["end_datetime"]
            or item.properties["start_datetime"]
        )
        links = item.links
        for link in links:
            if link.rel == "self":
                self_url = link.target
        assets_list = []
        assets = item.assets
        for asset in assets:
            assets_list.append(asset)

        # creating a list of lists of values
        items_list.append([id, geometry, datetime, self_url, assets_list])

    if limit is not None:
        items_list = items_list[:limit]
    items_df = gpd.GeoDataFrame(items_list)
    items_df.columns = ["id", "geometry", "datetime", "self_url", "assets_list"]

    items_gdf = items_df.set_geometry("geometry")
    items_gdf["datetime"] = items_gdf["datetime"].astype(
        str
    )  # specifically for KeplerGL. See https://github.com/keplergl/kepler.gl/issues/602
    # items_gdf["assets_list"] = items_gdf["assets_list"].astype(str) #specifically for KeplerGL. See https://github.com/keplergl/kepler.gl/issues/602
    items_gdf.set_crs(epsg=4326, inplace=True)
    return items_gdf


def list_palettes(add_extra=False, lowercase=False):
    """List all available colormaps. See a complete lost of colormaps at https://matplotlib.org/stable/tutorials/colors/colormaps.html.

    Returns:
        The list of colormap names.
    """
    import matplotlib.pyplot as plt

    result = plt.colormaps()
    if add_extra:
        result += ["dem", "ndvi", "ndwi"]
    if lowercase:
        result = [i.lower() for i in result]
    result.sort()
    return result


def get_palette_colors(cmap_name=None, n_class=None, hashtag=False):
    """Get a palette from a matplotlib colormap. See the list of colormaps at https://matplotlib.org/stable/tutorials/colors/colormaps.html.

    Args:
        cmap_name (str, optional): The name of the matplotlib colormap. Defaults to None.
        n_class (int, optional): The number of colors. Defaults to None.
        hashtag (bool, optional): Whether to return a list of hex colors. Defaults to False.

    Returns:
        A list of hex colors.
    """
    import matplotlib as mpl
    import matplotlib.pyplot as plt

    try:
        cmap = plt.get_cmap(cmap_name, n_class)
    except:
        cmap = plt.cm.get_cmap(cmap_name, n_class)
    colors = [mpl.colors.rgb2hex(cmap(i))[1:] for i in range(cmap.N)]
    if hashtag:
        colors = ["#" + i for i in colors]
    return colors


def mosaic_tile(url, titiler_endpoint=None, **kwargs: Any):
    """Get the tile URL from a MosaicJSON.

    Args:
        url (str): HTTP URL to a MosaicJSON.
        titiler_endpoint (str, optional): TiTiler endpoint, e.g., "https://giswqs-titiler-endpoint.hf.space". Defaults to None.

    Returns:
        The tile URL.
    """

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)

    if isinstance(url, str) and url.startswith("http"):
        kwargs["url"] = url
    else:
        raise ValueError("url must be a string and start with http.")

    if isinstance(titiler_endpoint, str):
        r = requests.get(
            f"{titiler_endpoint}/mosaicjson/tilejson.json",
            params=kwargs,
        ).json()
    else:
        raise ValueError("titiler_endpoint must be a string.")

    return r["tiles"][0]


def mosaic_bounds(url, titiler_endpoint=None, **kwargs: Any):
    """Get the bounding box of a MosaicJSON.

    Args:
        url (str): HTTP URL to a MosaicJSON.
        titiler_endpoint (str, optional): TiTiler endpoint, e.g., "https://giswqs-titiler-endpoint.hf.space". Defaults to None.

    Returns:
        A list of values representing [left, bottom, right, top].
    """

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)

    if isinstance(url, str) and url.startswith("http"):
        kwargs["url"] = url
    else:
        raise ValueError("url must be a string and start with http.")

    if isinstance(titiler_endpoint, str):
        r = requests.get(
            f"{titiler_endpoint}/mosaicjson/bounds",
            params=kwargs,
        ).json()
    else:
        raise ValueError("titiler_endpoint must be a string.")

    return r["bounds"]


def mosaic_info(url, titiler_endpoint=None, **kwargs: Any):
    """Get the info of a MosaicJSON.

    Args:
        url (str): HTTP URL to a MosaicJSON.
        titiler_endpoint (str, optional): TiTiler endpoint, e.g., "https://giswqs-titiler-endpoint.hf.space". Defaults to None.

    Returns:
        A dictionary containing bounds, center, minzoom, maxzoom, and name as keys.
    """

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)

    if isinstance(url, str) and url.startswith("http"):
        kwargs["url"] = url
    else:
        raise ValueError("url must be a string and start with http.")

    if isinstance(titiler_endpoint, str):
        r = requests.get(
            f"{titiler_endpoint}/mosaicjson/info",
            params=kwargs,
        ).json()
    else:
        raise ValueError("titiler_endpoint must be a string.")

    return r


def mosaic_info_geojson(url, titiler_endpoint=None, **kwargs: Any):
    """Get the info of a MosaicJSON.

    Args:
        url (str): HTTP URL to a MosaicJSON.
        titiler_endpoint (str, optional): TiTiler endpoint, e.g., "https://giswqs-titiler-endpoint.hf.space". Defaults to None.

    Returns:
        A dictionary representing a dict of GeoJSON.
    """

    titiler_endpoint = check_titiler_endpoint(titiler_endpoint)

    if isinstance(url, str) and url.startswith("http"):
        kwargs["url"] = url
    else:
        raise ValueError("url must be a string and start with http.")

    if isinstance(titiler_endpoint, str):
        r = requests.get(
            f"{titiler_endpoint}/mosaicjson/info.geojson",
            params=kwargs,
        ).json()
    else:
        raise ValueError("titiler_endpoint must be a string.")

    return r


def view_lidar(
    filename,
    cmap="terrain",
    backend="pyvista",
    background=None,
    eye_dome_lighting=False,
    **kwargs: Any,
):
    """View LiDAR data in 3D.

    Args:
        filename (str): The filepath to the LiDAR data.
        cmap (str, optional): The colormap to use. Defaults to "terrain". cmap currently does not work for the open3d backend.
        backend (str, optional): The plotting backend to use, can be pyvista, ipygany, panel, and open3d. Defaults to "pyvista".
        background (str, optional): The background color to use. Defaults to None.
        eye_dome_lighting (bool, optional): Whether to use eye dome lighting. Defaults to False.

    Raises:
        FileNotFoundError: If the file does not exist.
        ValueError: If the backend is not supported.
    """

    import sys

    if os.environ.get("USE_MKDOCS") is not None:
        return

    if "google.colab" in sys.modules:
        print("This function is not supported in Google Colab.")
        return

    warnings.filterwarnings("ignore")
    filename = os.path.abspath(filename)
    if not os.path.exists(filename):
        raise FileNotFoundError(f"{filename} does not exist.")

    backend = backend.lower()
    if backend in ["pyvista", "ipygany", "panel"]:
        try:
            import pyntcloud
        except ImportError:
            print(
                "The pyvista and pyntcloud packages are required for this function. Use pip install leafmap[lidar] to install them."
            )
            return

        try:
            if backend == "pyvista":
                backend = None
            if backend == "ipygany":
                cmap = None
            data = pyntcloud.PyntCloud.from_file(filename)
            mesh = data.to_instance("pyvista", mesh=False)
            mesh = mesh.elevation()
            mesh.plot(
                scalars="Elevation",
                cmap=cmap,
                jupyter_backend=backend,
                background=background,
                eye_dome_lighting=eye_dome_lighting,
                **kwargs,
            )

        except Exception as e:
            print("Something went wrong.")
            print(e)
            return

    elif backend == "open3d":
        try:
            import laspy
            import numpy as np
            import open3d as o3d
        except ImportError:
            print(
                "The laspy and open3d packages are required for this function. Use pip install laspy open3d to install them."
            )
            return

        try:
            las = laspy.read(filename)
            point_data = np.stack([las.X, las.Y, las.Z], axis=0).transpose((1, 0))
            geom = o3d.geometry.PointCloud()
            geom.points = o3d.utility.Vector3dVector(point_data)
            # geom.colors =  o3d.utility.Vector3dVector(colors)  # need to add colors. A list in the form of [[r,g,b], [r,g,b]] with value range 0-1. https://github.com/isl-org/Open3D/issues/614
            o3d.visualization.draw_geometries([geom], **kwargs)

        except Exception as e:
            print("Something went wrong.")
            print(e)
            return

    else:
        raise ValueError(f"{backend} is not a valid backend.")


def read_lidar(filename, **kwargs: Any):
    """Read a LAS file.

    Args:
        filename (str): A local file path or HTTP URL to a LAS file.

    Returns:
        The LasData object returned by laspy.read.
    """
    try:
        import laspy
    except ImportError:
        print(
            "The laspy package is required for this function. Use `pip install laspy[lazrs,laszip]` to install it."
        )
        return

    if (
        isinstance(filename, str)
        and filename.startswith("http")
        and (filename.endswith(".las") or filename.endswith(".laz"))
    ):
        filename = github_raw_url(filename)
        filename = download_file(filename)

    return laspy.read(filename, **kwargs)


def convert_lidar(
    source, destination=None, point_format_id=None, file_version=None, **kwargs
) -> Any:
    """Converts a Las from one point format to another Automatically upgrades the file version if source file version
        is not compatible with the new point_format_id

    Args:
        source (str | laspy.lasdatas.base.LasBase): The source data to be converted.
        destination (str, optional): The destination file path. Defaults to None.
        point_format_id (int, optional): The new point format id (the default is None, which won't change the source format id).
        file_version (str, optional): The new file version. None by default which means that the file_version may be upgraded
            for compatibility with the new point_format. The file version will not be downgraded.

    Returns:
        The converted LasData object.
    """
    try:
        import laspy
    except ImportError:
        print(
            "The laspy package is required for this function. Use `pip install laspy[lazrs,laszip]` to install it."
        )
        return

    if isinstance(source, str):
        source = read_lidar(source)

    las = laspy.convert(
        source, point_format_id=point_format_id, file_version=file_version
    )

    if destination is None:
        return las
    else:
        destination = check_file_path(destination)
        write_lidar(las, destination, **kwargs)
        return destination


def write_lidar(source, destination, do_compress=None, laz_backend=None):
    """Writes to a stream or file.

    Args:
        source (str | laspy.lasdatas.base.LasBase): The source data to be written.
        destination (str): The destination filepath.
        do_compress (bool, optional): Flags to indicate if you want to compress the data. Defaults to None.
        laz_backend (str, optional): The laz backend to use. Defaults to None.
    """

    try:
        import laspy
    except ImportError:
        print(
            "The laspy package is required for this function. Use `pip install laspy[lazrs,laszip]` to install it."
        )
        return

    if isinstance(source, str):
        source = read_lidar(source)

    source.write(destination, do_compress=do_compress, laz_backend=laz_backend)


def download_file(
    url=None,
    output=None,
    quiet=False,
    proxy=None,
    speed=None,
    use_cookies=True,
    verify=True,
    id=None,
    fuzzy=False,
    resume=False,
    unzip=True,
    overwrite=False,
    subfolder=False,
) -> str:
    """Download a file from URL, including Google Drive shared URL.

    Args:
        url (str, optional): Google Drive URL is also supported. Defaults to None.
        output (str, optional): Output filename. Default is basename of URL.
        quiet (bool, optional): Suppress terminal output. Default is False.
        proxy (str, optional): Proxy. Defaults to None.
        speed (float, optional): Download byte size per second (e.g., 256KB/s = 256 * 1024). Defaults to None.
        use_cookies (bool, optional): Flag to use cookies. Defaults to True.
        verify (bool | str, optional): Either a bool, in which case it controls whether the server's TLS certificate is verified, or a string,
            in which case it must be a path to a CA bundle to use. Default is True.. Defaults to True.
        id (str, optional): Google Drive's file ID. Defaults to None.
        fuzzy (bool, optional): Fuzzy extraction of Google Drive's file Id. Defaults to False.
        resume (bool, optional): Resume the download from existing tmp file if possible. Defaults to False.
        unzip (bool, optional): Unzip the file. Defaults to True.
        overwrite (bool, optional): Overwrite the file if it already exists. Defaults to False.
        subfolder (bool, optional): Create a subfolder with the same name as the file. Defaults to False.

    Returns:
        The output file path.
    """
    try:
        import gdown
    except ImportError:
        print(
            "The gdown package is required for this function. Use `pip install gdown` to install it."
        )
        return

    if output is None:
        if isinstance(url, str) and url.startswith("http"):
            output = os.path.basename(url)

    out_dir = os.path.abspath(os.path.dirname(output))
    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    if isinstance(url, str):
        if os.path.exists(os.path.abspath(output)) and (not overwrite):
            print(
                f"{output} already exists. Skip downloading. Set overwrite=True to overwrite."
            )
            return os.path.abspath(output)
        else:
            url = github_raw_url(url)

    if "https://drive.google.com/file/d/" in url:
        fuzzy = True

    output = gdown.download(
        url, output, quiet, proxy, speed, use_cookies, verify, id, fuzzy, resume
    )

    if unzip:
        if output.endswith(".zip"):
            with zipfile.ZipFile(output, "r") as zip_ref:
                if not quiet:
                    print("Extracting files...")
                if subfolder:
                    basename = os.path.splitext(os.path.basename(output))[0]

                    output = os.path.join(out_dir, basename)
                    if not os.path.exists(output):
                        os.makedirs(output)
                    zip_ref.extractall(output)
                else:
                    zip_ref.extractall(os.path.dirname(output))
        elif output.endswith(".tar.gz") or output.endswith(".tar"):
            if output.endswith(".tar.gz"):
                mode = "r:gz"
            else:
                mode = "r"

            with tarfile.open(output, mode) as tar_ref:
                if not quiet:
                    print("Extracting files...")
                if subfolder:
                    basename = os.path.splitext(os.path.basename(output))[0]
                    output = os.path.join(out_dir, basename)
                    if not os.path.exists(output):
                        os.makedirs(output)
                    tar_ref.extractall(output)
                else:
                    tar_ref.extractall(os.path.dirname(output))

    return os.path.abspath(output)


def download_files(
    urls,
    out_dir=None,
    filenames=None,
    quiet=False,
    proxy=None,
    speed=None,
    use_cookies=True,
    verify=True,
    id=None,
    fuzzy=False,
    resume=False,
    unzip=True,
    overwrite=False,
    subfolder=False,
    multi_part=False,
):
    """Download files from URLs, including Google Drive shared URL.

    Args:
        urls (list): The list of urls to download. Google Drive URL is also supported.
        out_dir (str, optional): The output directory. Defaults to None.
        filenames (list, optional): Output filename. Default is basename of URL.
        quiet (bool, optional): Suppress terminal output. Default is False.
        proxy (str, optional): Proxy. Defaults to None.
        speed (float, optional): Download byte size per second (e.g., 256KB/s = 256 * 1024). Defaults to None.
        use_cookies (bool, optional): Flag to use cookies. Defaults to True.
        verify (bool | str, optional): Either a bool, in which case it controls whether the server's TLS certificate is verified, or a string, in which case it must be a path to a CA bundle to use. Default is True.. Defaults to True.
        id (str, optional): Google Drive's file ID. Defaults to None.
        fuzzy (bool, optional): Fuzzy extraction of Google Drive's file Id. Defaults to False.
        resume (bool, optional): Resume the download from existing tmp file if possible. Defaults to False.
        unzip (bool, optional): Unzip the file. Defaults to True.
        overwrite (bool, optional): Overwrite the file if it already exists. Defaults to False.
        subfolder (bool, optional): Create a subfolder with the same name as the file. Defaults to False.
        multi_part (bool, optional): If the file is a multi-part file. Defaults to False.

    Examples:

        files = ["sam_hq_vit_tiny.zip", "sam_hq_vit_tiny.z01", "sam_hq_vit_tiny.z02", "sam_hq_vit_tiny.z03"]
        base_url = "https://github.com/opengeos/datasets/releases/download/models/"
        urls = [base_url + f for f in files]
        leafmap.download_files(urls, out_dir="models", multi_part=True)
    """

    if out_dir is None:
        out_dir = os.getcwd()

    if filenames is None:
        filenames = [None] * len(urls)

    filepaths = []
    for url, output in zip(urls, filenames):
        if output is None:
            filename = os.path.join(out_dir, os.path.basename(url))
        else:
            filename = os.path.join(out_dir, output)

        filepaths.append(filename)
        if multi_part:
            unzip = False

        download_file(
            url,
            filename,
            quiet,
            proxy,
            speed,
            use_cookies,
            verify,
            id,
            fuzzy,
            resume,
            unzip,
            overwrite,
            subfolder,
        )

    if multi_part:
        archive = os.path.splitext(filename)[0] + ".zip"
        out_dir = os.path.dirname(filename)
        extract_archive(archive, out_dir)

        for file in filepaths:
            os.remove(file)


def download_folder(
    url=None,
    id=None,
    output=None,
    quiet=False,
    proxy=None,
    speed=None,
    use_cookies=True,
    remaining_ok=False,
) -> Optional[List[str]]:
    """Downloads the entire folder from URL.

    Args:
        url (str, optional): URL of the Google Drive folder. Must be of the format 'https://drive.google.com/drive/folders/{url}'. Defaults to None.
        id (str, optional): Google Drive's folder ID. Defaults to None.
        output (str, optional):  String containing the path of the output folder. Defaults to current working directory.
        quiet (bool, optional): Suppress terminal output. Defaults to False.
        proxy (str, optional): Proxy. Defaults to None.
        speed (float, optional): Download byte size per second (e.g., 256KB/s = 256 * 1024). Defaults to None.
        use_cookies (bool, optional): Flag to use cookies. Defaults to True.

    Returns:
        List of files downloaded, or None if failed.
    """

    try:
        import gdown
    except ImportError:
        print(
            "The gdown package is required for this function. Use `pip install gdown` to install it."
        )
        return

    files = gdown.download_folder(
        url, id, output, quiet, proxy, speed, use_cookies, remaining_ok
    )
    return files


def clip_image(image, mask, output, to_cog=True):
    """Clip an image by mask.

    Args:
        image (str): Path to the image file in GeoTIFF format.
        mask (str | list | dict): The mask used to extract the image. It can be a path to vector datasets (e.g., GeoJSON, Shapefile), a list of coordinates, or m.user_roi.
        output (str): Path to the output file.
        to_cog (bool, optional): Flags to indicate if you want to convert the output to COG. Defaults to True.

    Raises:
        ImportError: If the fiona or rasterio package is not installed.
        FileNotFoundError: If the image is not found.
        ValueError: If the mask is not a valid GeoJSON or raster file.
        FileNotFoundError: If the mask file is not found.
    """
    try:
        import json

        import fiona
        import rasterio
        import rasterio.mask
    except ImportError as e:
        raise ImportError(e)

    if not os.path.exists(image):
        raise FileNotFoundError(f"{image} does not exist.")

    if not output.endswith(".tif"):
        raise ValueError("Output must be a tif file.")

    output = check_file_path(output)

    if isinstance(mask, str):
        if mask.startswith("http"):
            mask = download_file(mask, output)
        if not os.path.exists(mask):
            raise FileNotFoundError(f"{mask} does not exist.")
    elif isinstance(mask, list) or isinstance(mask, dict):
        if isinstance(mask, list):
            geojson = {
                "type": "FeatureCollection",
                "features": [
                    {
                        "type": "Feature",
                        "properties": {},
                        "geometry": {"type": "Polygon", "coordinates": [mask]},
                    }
                ],
            }
        else:
            geojson = {
                "type": "FeatureCollection",
                "features": [mask],
            }
        mask = temp_file_path(".geojson")
        with open(mask, "w") as f:
            json.dump(geojson, f)

    with fiona.open(mask, "r") as shapefile:
        shapes = [feature["geometry"] for feature in shapefile]

    with rasterio.open(image) as src:
        out_image, out_transform = rasterio.mask.mask(src, shapes, crop=True)
        out_meta = src.meta

    out_meta.update(
        {
            "driver": "GTiff",
            "height": out_image.shape[1],
            "width": out_image.shape[2],
            "transform": out_transform,
        }
    )

    with rasterio.open(output, "w", **out_meta) as dest:
        dest.write(out_image)

    if to_cog:
        image_to_cog(output, output)


def clip_raster(
    image,
    geometry,
    geom_crs=None,
    dst_crs=None,
    resolution=None,
    driver="COG",
    compress="DEFLATE",
    bands=None,
    match_raster=None,
    output=None,
    verbose=True,
    **kwargs: Any,
):
    """Clip a raster by a geometry.

    Args:
        image (str): Path to the image file in GeoTIFF format.
        geometry (str | geopandas.GeoDataFrame | dict | list): The geometry to clip the raster by.
            It can be a path to vector datasets (e.g., GeoJSON, Shapefile), a geopandas.GeoDataFrame,
            a dictionary, or a list of 4 numbers (minx, miny, maxx, maxy) representing a bounding box.
        geom_crs (str, optional): The coordinate reference system of the geometry. Defaults to None.
        dst_crs (str, optional): The coordinate reference system of the output raster. Defaults to None.
        resolution (int, optional): The resolution of the output raster. Defaults to None.
            If a single value is provided, the resolution will be the same in both x and y directions.
            If a tuple of two values is provided, the first value will be the resolution in the x
            direction and the second value will be the resolution in the y direction.
        driver (str, optional): The driver of the output raster. Defaults to "COG".
        compress (str, optional): The compression of the output raster. Defaults to "DEFLATE".
        bands (list, optional): The indices of the bands to clip. Defaults to None. Start from 1.
        match_raster (str, optional): Path to the raster to match the resolution and CRS of the output raster. Defaults to None.
        output (str, optional): Path to the output raster file. Defaults to None.
        **kwargs: Additional keyword arguments to pass to rasterio.reproject.
    """
    import geopandas as gpd
    import rioxarray as rxr
    import xarray as xr

    if match_raster is not None:
        if isinstance(match_raster, str):
            match_raster = rxr.open_rasterio(match_raster)
        elif isinstance(match_raster, xr.DataArray):
            match_raster = match_raster
        else:
            raise ValueError("match_raster must be a string or xarray.DataArray")

    if isinstance(image, str):
        xds = rxr.open_rasterio(image)
    elif isinstance(image, xr.DataArray):
        xds = image
    else:
        raise ValueError("image must be a string or xarray.DataArray")

    if bands is not None:
        xds = xds.sel(band=bands)

    if match_raster is not None:
        xds = xds.rio.reproject_match(match_raster)

    if isinstance(geometry, str):
        gdf = gpd.read_file(geometry)
    elif isinstance(geometry, gpd.GeoDataFrame):
        gdf = geometry
    elif isinstance(geometry, dict) and geometry.get("type") == "FeatureCollection":
        gdf = gpd.GeoDataFrame.from_features(geometry)
    elif isinstance(geometry, dict) and geometry.get("type") == "Feature":
        gdf = gpd.GeoDataFrame.from_features([geometry])
    elif isinstance(geometry, list) and len(geometry) == 4:
        gdf = bbox_to_gdf(geometry)
    else:
        raise ValueError(
            "geometry must be a string, geopandas.GeoDataFrame, dict, or list of 4 numbers"
        )

    if geom_crs is not None:
        gdf.set_crs(geom_crs, inplace=True)

    if isinstance(resolution, (int, float)):
        resolution = (resolution, resolution)
    elif isinstance(resolution, tuple) and len(resolution) == 2:
        pass
    elif resolution is not None:
        raise ValueError("resolution must be a single value or a tuple of two values")

    if dst_crs is None:
        dst_crs = xds.rio.crs

    if resolution is not None and dst_crs is not None:
        if verbose:
            print(
                f"Reprojecting the raster to {dst_crs} at {resolution} resolution ..."
            )
        xds = xds.rio.reproject(dst_crs, resolution=resolution, **kwargs)
    elif resolution is not None:
        if verbose:
            print(
                f"Reprojecting the raster to {dst_crs} at {resolution} resolution ..."
            )
        xds = xds.rio.reproject(dst_crs, resolution=resolution, **kwargs)
    elif dst_crs is not None and dst_crs != xds.rio.crs:
        if verbose:
            print(f"Reprojecting the raster to {dst_crs} ...")
        xds = xds.rio.reproject(dst_crs, **kwargs)
    else:
        pass

    gdf = gdf.to_crs(dst_crs)

    if verbose:
        print(f"Clipping the raster to the geometry ...")
    xds = xds.rio.clip(gdf.geometry, gdf.crs)
    if output is not None:
        if verbose:
            print(f"Saving the raster to {output} ...")
        xds.rio.to_raster(output, driver=driver, compress=compress)
    if verbose:
        print(f"The output raster is saved to {output}")
    return xds


def netcdf_to_tif(
    filename,
    output=None,
    variables=None,
    shift_lon=True,
    lat="lat",
    lon="lon",
    lev="lev",
    level_index=0,
    time=0,
    crs="epsg:4326",
    return_vars=False,
    **kwargs: Any,
):
    """Convert a netcdf file to a GeoTIFF file.

    Args:
        filename (str): Path to the netcdf file.
        output (str, optional): Path to the output GeoTIFF file. Defaults to None. If None, the output file will be the same as the input file with the extension changed to .tif.
        variables (str | list, optional): Name of the variable or a list of variables to extract. Defaults to None. If None, all variables will be extracted.
        shift_lon (bool, optional): Flag to shift longitude values from [0, 360] to the range [-180, 180]. Defaults to True.
        lat (str, optional): Name of the latitude variable. Defaults to 'lat'.
        lon (str, optional): Name of the longitude variable. Defaults to 'lon'.
        lev (str, optional): Name of the level variable. Defaults to 'lev'.
        level_index (int, optional): Index of the level dimension. Defaults to 0'.
        time (int, optional): Index of the time dimension. Defaults to 0'.
        crs (str, optional): The coordinate reference system. Defaults to 'epsg:4326'.
        return_vars (bool, optional): Flag to return all variables. Defaults to False.

    Raises:
        ImportError: If the xarray or rioxarray package is not installed.
        FileNotFoundError: If the netcdf file is not found.
        ValueError: If the variable is not found in the netcdf file.
    """
    try:
        import xarray as xr
    except ImportError as e:
        raise ImportError(e)

    if filename.startswith("http"):
        filename = download_file(filename)

    if not os.path.exists(filename):
        raise FileNotFoundError(f"{filename} does not exist.")

    if output is None:
        ext = os.path.splitext(filename)[1].lower()
        if ext not in [".nc", ".nc4"]:
            raise TypeError(
                "The output file must be a netCDF with extension .nc or .nc4."
            )
        output = filename.replace(ext, ".tif")
    else:
        output = check_file_path(output)

    xds = xr.open_dataset(filename, **kwargs)

    coords = list(xds.coords.keys())
    if "time" in coords:
        xds = xds.isel(time=time, drop=True)

    if lev in coords:
        xds = xds.isel(lev=level_index, drop=True)

    if shift_lon:
        xds.coords[lon] = (xds.coords[lon] + 180) % 360 - 180
        xds = xds.sortby(xds[lon])

    allowed_vars = list(xds.data_vars.keys())
    if isinstance(variables, str):
        if variables not in allowed_vars:
            raise ValueError(f"{variables} is not a valid variable.")
        variables = [variables]

    if variables is not None and (not set(variables).issubset(allowed_vars)):
        raise ValueError(f"{variables} must be a subset of {allowed_vars}.")

    if variables is None:
        xds.rio.set_spatial_dims(x_dim=lon, y_dim=lat).rio.write_crs(crs).rio.to_raster(
            output
        )
    else:
        xds[variables].rio.set_spatial_dims(x_dim=lon, y_dim=lat).rio.write_crs(
            crs
        ).rio.to_raster(output)

    if return_vars:
        return output, allowed_vars
    else:
        return output


def read_netcdf(filename, **kwargs: Any):
    """Read a netcdf file.

    Args:
        filename (str): File path or HTTP URL to the netcdf file.

    Raises:
        ImportError: If the xarray or rioxarray package is not installed.
        FileNotFoundError: If the netcdf file is not found.

    Returns:
        The netcdf file as an xarray dataset.
    """
    try:
        import xarray as xr
    except ImportError as e:
        raise ImportError(e)

    if filename.startswith("http"):
        filename = download_file(filename)

    if not os.path.exists(filename):
        raise FileNotFoundError(f"{filename} does not exist.")

    xds = xr.open_dataset(filename, **kwargs)
    return xds


def netcdf_tile_layer(
    filename,
    variables=None,
    colormap=None,
    vmin=None,
    vmax=None,
    nodata=None,
    port="default",
    debug=False,
    attribution=None,
    tile_format="ipyleaflet",
    layer_name="NetCDF layer",
    return_client=False,
    shift_lon=True,
    lat="lat",
    lon="lon",
    **kwargs: Any,
):
    """Generate an ipyleaflet/folium TileLayer from a netCDF file.
        If you are using this function in JupyterHub on a remote server (e.g., Binder, Microsoft Planetary Computer),
        try adding to following two lines to the beginning of the notebook if the raster does not render properly.

        import os
        os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = f'{os.environ['JUPYTERHUB_SERVICE_PREFIX'].lstrip('/')}/proxy/{{port}}'

    Args:
        filename (str): File path or HTTP URL to the netCDF file.
        variables (int, optional): The variable/band names to extract data from the netCDF file. Defaults to None. If None, all variables will be extracted.
        port (str, optional): The port to use for the server. Defaults to "default".
        colormap (str, optional): The name of the colormap from `matplotlib` to use when plotting a single band. See https://matplotlib.org/stable/gallery/color/colormap_reference.html. Default is greyscale.
        vmin (float, optional): The minimum value to use when colormapping the colormap when plotting a single band. Defaults to None.
        vmax (float, optional): The maximum value to use when colormapping the colormap when plotting a single band. Defaults to None.
        nodata (float, optional): The value from the band to use to interpret as not valid data. Defaults to None.
        debug (bool, optional): If True, the server will be started in debug mode. Defaults to False.
        attribution (str, optional): Attribution for the source raster. This defaults to a message about it being a local file.. Defaults to None.
        tile_format (str, optional): The tile layer format. Can be either ipyleaflet or folium. Defaults to "ipyleaflet".
        layer_name (str, optional): The layer name to use. Defaults to "NetCDF layer".
        return_client (bool, optional): If True, the tile client will be returned. Defaults to False.
        shift_lon (bool, optional): Flag to shift longitude values from [0, 360] to the range [-180, 180]. Defaults to True.
        lat (str, optional): Name of the latitude variable. Defaults to 'lat'.
        lon (str, optional): Name of the longitude variable. Defaults to 'lon'.

    Returns:
        An ipyleaflet.TileLayer or folium.TileLayer.
    """

    check_package(
        "localtileserver", URL="https://github.com/banesullivan/localtileserver"
    )

    try:
        import xarray as xr
    except ImportError as e:
        raise ImportError(e)

    if filename.startswith("http"):
        filename = download_file(filename)

    if not os.path.exists(filename):
        raise FileNotFoundError(f"{filename} does not exist.")

    output = filename.replace(".nc", ".tif")

    xds = xr.open_dataset(filename, **kwargs)

    if shift_lon:
        xds.coords[lon] = (xds.coords[lon] + 180) % 360 - 180
        xds = xds.sortby(xds.lon)

    allowed_vars = list(xds.data_vars.keys())
    if isinstance(variables, str):
        if variables not in allowed_vars:
            raise ValueError(f"{variables} is not a subset of {allowed_vars}.")
        variables = [variables]

    if variables is not None and len(variables) > 3:
        raise ValueError("Only 3 variables can be plotted at a time.")

    if variables is not None and (not set(variables).issubset(allowed_vars)):
        raise ValueError(f"{variables} must be a subset of {allowed_vars}.")

    xds.rio.set_spatial_dims(x_dim=lon, y_dim=lat).rio.to_raster(output)
    if variables is None:
        if len(allowed_vars) >= 3:
            band_idx = [1, 2, 3]
        else:
            band_idx = [1]
    else:
        band_idx = [allowed_vars.index(var) + 1 for var in variables]

    tile_layer = get_local_tile_layer(
        output,
        port=port,
        debug=debug,
        indexes=band_idx,
        colormap=colormap,
        vmin=vmin,
        vmax=vmax,
        nodata=nodata,
        attribution=attribution,
        tile_format=tile_format,
        layer_name=layer_name,
        return_client=return_client,
    )
    return tile_layer


def classify(
    data,
    column,
    cmap=None,
    colors=None,
    labels=None,
    scheme="Quantiles",
    k=5,
    legend_kwds=None,
    classification_kwds=None,
) -> Tuple[Any, Dict[str, Any]]:
    """Classify a dataframe column using a variety of classification schemes.

    Args:
        data (str | pd.DataFrame | gpd.GeoDataFrame): The data to classify. It can be a filepath to a vector dataset, a pandas dataframe, or a geopandas geodataframe.
        column (str): The column to classify.
        cmap (str, optional): The name of a colormap recognized by matplotlib. Defaults to None.
        colors (list, optional): A list of colors to use for the classification. Defaults to None.
        labels (list, optional): A list of labels to use for the legend. Defaults to None.
        scheme (str, optional): Name of a choropleth classification scheme (requires mapclassify).
            Name of a choropleth classification scheme (requires mapclassify).
            A mapclassify.MapClassifier object will be used
            under the hood. Supported are all schemes provided by mapclassify (e.g.
            'BoxPlot', 'EqualInterval', 'FisherJenks', 'FisherJenksSampled',
            'HeadTailBreaks', 'JenksCaspall', 'JenksCaspallForced',
            'JenksCaspallSampled', 'MaxP', 'MaximumBreaks',
            'NaturalBreaks', 'Quantiles', 'Percentiles', 'StdMean',
            'UserDefined'). Arguments can be passed in classification_kwds.
        k (int, optional): Number of classes (ignored if scheme is None or if column is categorical). Default to 5.
        legend_kwds (dict, optional): Keyword arguments to pass to :func:`matplotlib.pyplot.legend` or `matplotlib.pyplot.colorbar`. Defaults to None.
            Keyword arguments to pass to :func:`matplotlib.pyplot.legend` or
            Additional accepted keywords when `scheme` is specified:
            fmt : string
                A formatting specification for the bin edges of the classes in the
                legend. For example, to have no decimals: ``{"fmt": "{:.0f}"}``.
            labels : list-like
                A list of legend labels to override the auto-generated labblels.
                Needs to have the same number of elements as the number of
                classes (`k`).
            interval : boolean (default False)
                An option to control brackets from mapclassify legend.
                If True, open/closed interval brackets are shown in the legend.
        classification_kwds (dict, optional): Keyword arguments to pass to mapclassify. Defaults to None.

    Returns:
        A pandas dataframe with the classification applied and a legend dictionary.
    """

    import geopandas as gpd
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd

    try:
        import mapclassify
    except ImportError:
        raise ImportError(
            "mapclassify is required for this function. Install with `pip install mapclassify`."
        )

    if (
        isinstance(data, gpd.GeoDataFrame)
        or isinstance(data, pd.DataFrame)
        or isinstance(data, pd.Series)
    ):
        # copy user provided data to avoid pandas SettingWithCopy warnings
        df = data.copy()
    else:
        try:
            df = gpd.read_file(data)
        except Exception:
            raise TypeError(
                "Data must be a GeoDataFrame or a path to a file that can be read by geopandas.read_file()."
            )

    if df.empty:
        warnings.warn(
            "The GeoDataFrame you are attempting to plot is "
            "empty. Nothing has been displayed.",
            UserWarning,
        )
        return

    columns = df.columns.values.tolist()
    if column not in columns:
        raise ValueError(
            f"{column} is not a column in the GeoDataFrame. It must be one of {columns}."
        )

    # Convert categorical data to numeric
    init_column = None
    value_list = None
    if np.issubdtype(df[column].dtype, np.object_):
        value_list = df[column].unique().tolist()
        value_list.sort()
        df["category"] = df[column].replace(value_list, range(0, len(value_list)))
        init_column = column
        column = "category"
        k = len(value_list)

    if legend_kwds is not None:
        legend_kwds = legend_kwds.copy()

    # To accept pd.Series and np.arrays as column
    if isinstance(column, (np.ndarray, pd.Series)):
        if column.shape[0] != df.shape[0]:
            raise ValueError(
                "The dataframe and given column have different number of rows."
            )
        else:
            values = column

            # Make sure index of a Series matches index of df
            if isinstance(values, pd.Series):
                values = values.reindex(df.index)
    else:
        values = df[column]

    values = df[column]
    nan_idx = np.asarray(pd.isna(values), dtype="bool")

    if cmap is None:
        cmap = "Blues"
    try:
        cmap = plt.get_cmap(cmap, k)
    except:
        cmap = plt.cm.get_cmap(cmap, k)
    if colors is None:
        colors = [mpl.colors.rgb2hex(cmap(i))[1:] for i in range(cmap.N)]
        colors = ["#" + i for i in colors]
    elif isinstance(colors, list):
        colors = [check_color(i) for i in colors]
    elif isinstance(colors, str):
        colors = [check_color(colors)] * k

    allowed_schemes = [
        "BoxPlot",
        "EqualInterval",
        "FisherJenks",
        "FisherJenksSampled",
        "HeadTailBreaks",
        "JenksCaspall",
        "JenksCaspallForced",
        "JenksCaspallSampled",
        "MaxP",
        "MaximumBreaks",
        "NaturalBreaks",
        "Quantiles",
        "Percentiles",
        "StdMean",
        "UserDefined",
    ]

    if scheme.lower() not in [s.lower() for s in allowed_schemes]:
        raise ValueError(
            f"{scheme} is not a valid scheme. It must be one of {allowed_schemes}."
        )

    if classification_kwds is None:
        classification_kwds = {}
    if "k" not in classification_kwds:
        classification_kwds["k"] = k

    binning = mapclassify.classify(
        np.asarray(values[~nan_idx]), scheme, **classification_kwds
    )
    df["category"] = np.nan
    df.loc[~nan_idx, "category"] = binning.yb
    df["color"] = None
    df.loc[~nan_idx, "color"] = [colors[int(i)] for i in df.loc[~nan_idx, "category"]]

    if legend_kwds is None:
        legend_kwds = {}

    if "interval" not in legend_kwds:
        legend_kwds["interval"] = True

    if "fmt" not in legend_kwds:
        if np.issubdtype(df[column].dtype, np.floating):
            legend_kwds["fmt"] = "{:.2f}"
        else:
            legend_kwds["fmt"] = "{:.0f}"

    if labels is None:
        # set categorical to True for creating the legend
        if legend_kwds is not None and "labels" in legend_kwds:
            if len(legend_kwds["labels"]) != binning.k:
                raise ValueError(
                    "Number of labels must match number of bins, "
                    "received {} labels for {} bins".format(
                        len(legend_kwds["labels"]), binning.k
                    )
                )
            else:
                labels = list(legend_kwds.pop("labels"))
        else:
            # fmt = "{:.2f}"
            if legend_kwds is not None and "fmt" in legend_kwds:
                fmt = legend_kwds.pop("fmt")

            labels = binning.get_legend_classes(fmt)
            if legend_kwds is not None:
                show_interval = legend_kwds.pop("interval", False)
            else:
                show_interval = False
            if not show_interval:
                labels = [c[1:-1] for c in labels]

        if init_column is not None:
            labels = value_list
    elif isinstance(labels, list):
        if len(labels) != len(colors):
            raise ValueError("The number of labels must match the number of colors.")
    else:
        raise ValueError("labels must be a list or None.")

    legend_dict = dict(zip(labels, colors))
    df.loc[~nan_idx, "category"] = df.loc[~nan_idx, "category"] + 1
    return df, legend_dict


def check_cmap(cmap) -> List[str]:
    """Check the colormap and return a list of colors.

    Args:
        cmap (str | list | Box): The colormap to check.

    Returns:
        A list of colors.
    """

    from box import Box

    from .colormaps import get_palette

    if isinstance(cmap, str):
        try:
            return get_palette(cmap)
        except Exception as e:
            raise Exception(f"{cmap} is not a valid colormap.")
    elif isinstance(cmap, Box):
        return list(cmap["default"])
    elif isinstance(cmap, list) or isinstance(cmap, tuple):
        return cmap
    else:
        raise Exception(f"{cmap} is not a valid colormap.")


def plot_raster(
    image,
    band=None,
    cmap="terrain",
    proj="EPSG:3857",
    figsize=None,
    open_kwargs={},
    **kwargs: Any,
):
    """Plot a raster image.

    Args:
        image (str | xarray.DataArray ): The input raster image, can be a file path, HTTP URL, or xarray.DataArray.
        band (int, optional): The band index, starting from zero. Defaults to None.
        cmap (str, optional): The matplotlib colormap to use. Defaults to "terrain".
        proj (str, optional): The EPSG projection code. Defaults to "EPSG:3857".
        figsize (tuple, optional): The figure size as a tuple, such as (10, 8). Defaults to None.
        open_kwargs (dict, optional): The keyword arguments to pass to rioxarray.open_rasterio. Defaults to {}.
        **kwargs: Additional keyword arguments to pass to xarray.DataArray.plot().

    """
    if os.environ.get("USE_MKDOCS") is not None:
        return

    try:
        import pvxarray
        import rioxarray
        import xarray
    except ImportError:
        print(
            "pyxarray and rioxarray are required for plotting. Please install them using 'pip install rioxarray pyvista-xarray'."
        )
        return

    if isinstance(image, str):
        da = rioxarray.open_rasterio(image, **open_kwargs)
    elif isinstance(image, xarray.DataArray):
        da = image
    else:
        raise ValueError("image must be a string or xarray.Dataset.")

    if band is not None:
        da = da[dict(band=band)]

    da = da.rio.reproject(proj)
    kwargs["cmap"] = cmap
    kwargs["figsize"] = figsize
    da.plot(**kwargs)


def plot_raster_3d(
    image,
    band=None,
    cmap="terrain",
    factor=1.0,
    proj="EPSG:3857",
    background=None,
    x=None,
    y=None,
    z=None,
    order=None,
    component=None,
    open_kwargs={},
    mesh_kwargs={},
    **kwargs: Any,
):
    """Plot a raster image in 3D.

    Args:
        image (str | xarray.DataArray): The input raster image, can be a file path, HTTP URL, or xarray.DataArray.
        band (int, optional): The band index, starting from zero. Defaults to None.
        cmap (str, optional): The matplotlib colormap to use. Defaults to "terrain".
        factor (float, optional): The scaling factor for the raster. Defaults to 1.0.
        proj (str, optional): The EPSG projection code. Defaults to "EPSG:3857".
        background (str, optional): The background color. Defaults to None.
        x (str, optional): The x coordinate. Defaults to None.
        y (str, optional): The y coordinate. Defaults to None.
        z (str, optional): The z coordinate. Defaults to None.
        order (str, optional): The order of the coordinates. Defaults to None.
        component (str, optional): The component of the coordinates. Defaults to None.
        open_kwargs (dict, optional): The keyword arguments to pass to rioxarray.open_rasterio. Defaults to {}.
        mesh_kwargs (dict, optional): The keyword arguments to pass to pyvista.mesh.warp_by_scalar(). Defaults to {}.
        **kwargs: Additional keyword arguments to pass to xarray.DataArray.plot().
    """
    import sys

    if os.environ.get("USE_MKDOCS") is not None:
        return

    if "google.colab" in sys.modules:
        print("This function is not supported in Google Colab.")
        return

    try:
        import pvxarray
        import pyvista
        import rioxarray
        import xarray
    except ImportError:
        print(
            "pyxarray and rioxarray are required for plotting. Please install them using 'pip install rioxarray pyvista-xarray'."
        )
        return

    if isinstance(background, str):
        pyvista.global_theme.background = background

    if isinstance(image, str):
        da = rioxarray.open_rasterio(image, **open_kwargs)
    elif isinstance(image, xarray.DataArray):
        da = image
    else:
        raise ValueError("image must be a string or xarray.Dataset.")

    if band is not None:
        da = da[dict(band=band)]

    da = da.rio.reproject(proj)
    mesh_kwargs["factor"] = factor
    kwargs["cmap"] = cmap

    coords = list(da.coords)

    if x is None:
        if "x" in coords:
            x = "x"
        elif "lon" in coords:
            x = "lon"
    if y is None:
        if "y" in coords:
            y = "y"
        elif "lat" in coords:
            y = "lat"
    if z is None:
        if "z" in coords:
            z = "z"
        elif "elevation" in coords:
            z = "elevation"
        elif "band" in coords:
            z = "band"

    # Grab the mesh object for use with PyVista
    mesh = da.pyvista.mesh(x=x, y=y, z=z, order=order, component=component)

    # Warp top and plot in 3D
    mesh.warp_by_scalar(**mesh_kwargs).plot(**kwargs)


def github_raw_url(url):
    """Get the raw URL for a GitHub file.

    Args:
        url (str): The GitHub URL.
    Returns:
        The raw URL.
    """
    if isinstance(url, str) and url.startswith("https://github.com/") and "blob" in url:
        url = url.replace("github.com", "raw.githubusercontent.com").replace(
            "blob/", ""
        )
    return url


def get_direct_url(url):
    """Get the direct URL for a given URL.

    Args:
        url (str): The URL to get the direct URL for.

    Returns:
        The direct URL.
    """

    if not isinstance(url, str):
        raise ValueError("url must be a string.")

    if not url.startswith("http"):
        raise ValueError("url must start with http.")

    r = requests.head(url, allow_redirects=True)
    return r.url


def add_crs(filename, epsg):
    """Add a CRS to a raster dataset.

    Args:
        filename (str): The filename of the raster dataset.
        epsg (int | str): The EPSG code of the CRS.

    """
    try:
        import rasterio
    except ImportError:
        raise ImportError(
            "rasterio is required for adding a CRS to a raster. Please install it using 'pip install rasterio'."
        )

    if not os.path.exists(filename):
        raise ValueError("filename must exist.")

    if isinstance(epsg, int):
        epsg = f"EPSG:{epsg}"
    elif isinstance(epsg, str):
        epsg = "EPSG:" + epsg
    else:
        raise ValueError("epsg must be an integer or string.")

    crs = rasterio.crs.CRS({"init": epsg})
    with rasterio.open(filename, mode="r+") as src:
        src.crs = crs


class The_national_map_USGS:
    """
    The national map is a collection of topological datasets, maintained by the USGS.

    It provides an API endpoint which can be used to find downloadable links for the products offered.
        - Full description of datasets available can retrieved.
          This consists of metadata such as detail description and publication dates.
        - A wide range of dataformats are available

    This class is a tiny wrapper to find and download files using the API.

    More complete documentation for the API can be found at
        https://apps.nationalmap.gov/tnmaccess/#/
    """

    def __init__(self):
        self.api_endpoint = r"https://tnmaccess.nationalmap.gov/api/v1/"
        self.DS = self.datasets_full

    @property
    def datasets_full(self) -> list:
        """
        Full description of datasets provided.
        Returns a JSON or empty list.
        """
        link = f"{self.api_endpoint}datasets?"
        try:
            return requests.get(link).json()
        except Exception:
            print(f"Failed to load metadata from The National Map API endpoint\n{link}")
            return []

    @property
    def prodFormats(self) -> list:
        """
        Return all datatypes available in any of the collections.
        Note that "All" is only peculiar to one dataset.
        """
        return set(i["displayName"] for ds in self.DS for i in ds["formats"])

    @property
    def datasets(self) -> list:
        """
        Returns a list of dataset tags (most common human readable self description for specific datasets).
        """
        return set(y["sbDatasetTag"] for x in self.DS for y in x["tags"])

    def parse_region(self, region, geopandas_args={}) -> list:
        """

        Translate a Vector dataset to its bounding box.

        Args:
            region (str | list): an URL|filepath to a vector dataset to a polygon
            geopandas_args (dict, optional): A dictionary of arguments to pass to the geopandas.read_file() function.
                Used for reading a region URL|filepath.
        """
        import geopandas as gpd

        if isinstance(region, str):
            if region.startswith("http"):
                region = github_raw_url(region)
                region = download_file(region)
            elif not os.path.exists(region):
                raise ValueError("region must be a path or a URL to a vector dataset.")

            roi = gpd.read_file(region, **geopandas_args)
            roi = roi.to_crs(epsg=4326)
            return roi.total_bounds
        return region

    def download_tiles(
        self, region=None, out_dir=None, download_args={}, geopandas_args={}, API={}
    ) -> None:
        """

        Download the US National Elevation Datasets (NED) for a region.

        Args:
            region (str | list, optional): An URL|filepath to a vector dataset Or a list of bounds in the form of [minx, miny, maxx, maxy].
                Alternatively you could use API parameters such as polygon or bbox.
            out_dir (str, optional): The directory to download the files to. Defaults to None, which uses the current working directory.
            download_args (dict, optional): A dictionary of arguments to pass to the download_file function. Defaults to {}.
            geopandas_args (dict, optional): A dictionary of arguments to pass to the geopandas.read_file() function.
                Used for reading a region URL|filepath.
            API (dict, optional): A dictionary of arguments to pass to the self.find_details() function.
                Exposes most of the documented API. Defaults to {}.

        Returns:
            None
        """

        if os.environ.get("USE_MKDOCS") is not None:
            return

        if out_dir is None:
            out_dir = os.getcwd()
        else:
            out_dir = os.path.abspath(out_dir)

        tiles = self.find_tiles(
            region, return_type="list", geopandas_args=geopandas_args, API=API
        )
        T = len(tiles)
        errors = 0
        done = 0

        for i, link in enumerate(tiles):
            file_name = os.path.basename(link)
            out_name = os.path.join(out_dir, file_name)
            if i < 5 or (i < 50 and not (i % 5)) or not (i % 20):
                print(f"Downloading {i+1} of {T}: {file_name}")
            try:
                download_file(link, out_name, **download_args)
                done += 1
            except KeyboardInterrupt:
                print("Cancelled download")
                break
            except Exception:
                errors += 1
                print(f"Failed to download {i+1} of {T}: {file_name}")

        print(
            f"{done} Downloads completed, {errors} downloads failed, {T} files available"
        )
        return

    def find_tiles(
        self, region=None, return_type="list", geopandas_args={}, API={}
    ) -> Union[List[str], Dict]:
        """
        Find a list of downloadable files.

        Args:
            region (str | list, optional): An URL|filepath to a vector dataset Or a list of bounds in the form of [minx, miny, maxx, maxy].
                Alternatively you could use API parameters such as polygon or bbox.
            return_type (str): list | dict. Defaults to list. Changes the return output type and content.
            geopandas_args (dict, optional): A dictionary of arguments to pass to the geopandas.read_file() function.
                Used for reading a region URL|filepath.
            API (dict, optional): A dictionary of arguments to pass to the self.find_details() function.
                Exposes most of the documented API parameters. Defaults to {}.

        Returns:
            A list of download URLs if return_type is 'list', otherwise a dictionary with URLs and related metadata.
        """
        assert region or API, "Provide a region or use the API"

        if region:
            API["bbox"] = self.parse_region(region, geopandas_args)

        results = self.find_details(**API)
        if return_type == "list":
            return [i["downloadURL"] for i in results.get("items")]
        return results

    def find_details(
        self,
        bbox: List[float] = None,
        polygon: List[Tuple[float, float]] = None,
        datasets: str = None,
        prodFormats: str = None,
        prodExtents: str = None,
        q: str = None,
        dateType: str = None,
        start: str = None,
        end: str = None,
        offset: int = 0,
        max: int = None,
        outputFormat: str = "JSON",
        polyType: str = None,
        polyCode: str = None,
        extentQuery: int = None,
    ) -> Dict:
        """
        Possible search parameters (kwargs) support by API

        Parameter               Values
            Description
        ---------------------------------------------------------------------------------------------------
        bbox                    'minx, miny, maxx, maxy'
            Geographic longitude/latitude values expressed in  decimal degrees in a comma-delimited list.
        polygon                 '[x,y x,y x,y x,y x,y]'
            Polygon, longitude/latitude values expressed in decimal degrees in a space-delimited list.
        datasets                See: Datasets (Optional)
            Dataset tag name (sbDatasetTag)
            From https://apps.nationalmap.gov/tnmaccess/#/product
        prodFormats             See: Product Formats (Optional)
            Dataset-specific format

        prodExtents             See: Product Extents (Optional)
            Dataset-specific extent
        q                       free text
            Text input which can be used to filter by product titles and text descriptions.
        dateType                dateCreated | lastUpdated | Publication
            Type of date to search by.
        start                   'YYYY-MM-DD'
            Start date
        end                     'YYYY-MM-DD'
            End date (required if start date is provided)
        offset                  integer
            Offset into paginated results - default=0
        max                     integer
            Number of results returned
        outputFormat            JSON | CSV | pjson
            Default=JSON
        polyType                state | huc2 | huc4 | huc8
            Well Known Polygon Type. Use this parameter to deliver data by state or HUC
            (hydrologic unit codes defined by the Watershed Boundary Dataset/WBD)
        polyCode                state FIPS code or huc number
            Well Known Polygon Code. This value needs to coordinate with the polyType parameter.
        extentQuery             integer
            A Polygon code in the science base system, typically from an uploaded shapefile
        """

        try:
            # call locals before creating new locals
            used_locals = {k: v for k, v in locals().items() if v and k != "self"}

            # Parsing
            if polygon:
                used_locals["polygon"] = ",".join(
                    " ".join(map(str, point)) for point in polygon
                )
            if bbox:
                used_locals["bbox"] = str(bbox)[1:-1]

            if max:
                max += 2

            # Fetch response
            response = requests.get(f"{self.api_endpoint}products?", params=used_locals)
            if response.status_code // 100 == 2:
                return response.json()
            else:
                # Parameter validation handled by API endpoint error responses
                print(response.json())
            return {}
        except Exception as e:
            print(e)
            return {}


def download_tnm(
    region=None,
    out_dir=None,
    return_url=False,
    download_args={},
    geopandas_args={},
    API={},
) -> Union[None, List]:
    """Download the US National Elevation Datasets (NED) for a region.

    Args:
        region (str | list, optional): An URL|filepath to a vector dataset Or a list of bounds in the form of [minx, miny, maxx, maxy].
            Alternatively you could use API parameters such as polygon or bbox.
        out_dir (str, optional): The directory to download the files to. Defaults to None, which uses the current working directory.
        return_url (bool, optional): Whether to return the download URLs of the files. Defaults to False.
        download_args (dict, optional): A dictionary of arguments to pass to the download_file function. Defaults to {}.
        geopandas_args (dict, optional): A dictionary of arguments to pass to the geopandas.read_file() function.
            Used for reading a region URL|filepath.
        API (dict, optional): A dictionary of arguments to pass to the The_national_map_USGS.find_details() function.
            Exposes most of the documented API. Defaults to {}

    Returns:
        A list of the download URLs of the files if return_url is True.
    """

    if os.environ.get("USE_MKDOCS") is not None:
        return

    TNM = The_national_map_USGS()
    if return_url:
        return TNM.find_tiles(region=region, geopandas_args=geopandas_args, API=API)
    return TNM.download_tiles(
        region=region,
        out_dir=out_dir,
        download_args=download_args,
        geopandas_args=geopandas_args,
        API=API,
    )


def download_ned(
    region,
    out_dir=None,
    return_url=False,
    download_args={},
    geopandas_args={},
    query={},
) -> Union[None, List]:
    """Download the US National Elevation Datasets (NED) for a region.

    Args:
        region (str | list): A filepath to a vector dataset or a list of bounds in the form of [minx, miny, maxx, maxy].
        out_dir (str, optional): The directory to download the files to. Defaults to None, which uses the current working directory.
        return_url (bool, optional): Whether to return the download URLs of the files. Defaults to False.
        download_args (dict, optional): A dictionary of arguments to pass to the download_file function. Defaults to {}.
        geopandas_args (dict, optional): A dictionary of arguments to pass to the geopandas.read_file() function.
            Used for reading a region URL|filepath.
        query (dict, optional): A dictionary of arguments to pass to the The_national_map_USGS.find_details() function.
            See https://apps.nationalmap.gov/tnmaccess/#/product for more information.

    Returns:
        A list of the download URLs of the files if return_url is True.
    """

    if os.environ.get("USE_MKDOCS") is not None:
        return

    if not query:
        query = {
            "datasets": "National Elevation Dataset (NED) 1/3 arc-second",
            "prodFormats": "GeoTIFF",
        }

    TNM = The_national_map_USGS()
    if return_url:
        return TNM.find_tiles(region=region, geopandas_args=geopandas_args, API=query)
    return TNM.download_tiles(
        region=region,
        out_dir=out_dir,
        download_args=download_args,
        geopandas_args=geopandas_args,
        API=query,
    )


def mosaic(
    images,
    output,
    ext="tif",
    recursive=True,
    merge_args={},
    to_cog=True,
    verbose=True,
    **kwargs: Any,
):
    """Mosaics a list of images into a single image. Inspired by https://bit.ly/3A6roDK.

    Args:
        images (str | list): An input directory containing images or a list of images.
        output (str): The output image filepath.
        ext (str, optional): The file extension of the images. Defaults to 'tif'.
        recursive (bool, optional): Whether to recursively search for images in the input directory. Defaults to True.
        merge_args (dict, optional): A dictionary of arguments to pass to the rasterio.merge function. Defaults to {}.
        to_cog (bool, optional): Whether to convert the output image to a Cloud Optimized GeoTIFF. Defaults to True.
        verbose (bool, optional): Whether to print progress. Defaults to True.

    """
    from pathlib import Path

    import rasterio as rio
    from rasterio.merge import merge

    output = os.path.abspath(output)

    if isinstance(images, str):
        raster_files = find_files(images, ext=ext, recursive=recursive)
    elif isinstance(images, list):
        raster_files = images
    else:
        raise ValueError("images must be a list of raster files.")

    raster_to_mosiac = []

    if not os.path.exists(os.path.dirname(output)):
        os.makedirs(os.path.dirname(output))

    for index, p in enumerate(raster_files):
        if verbose:
            print(f"Reading {index+1}/{len(raster_files)}: {os.path.basename(p)}")
        raster = rio.open(p, **kwargs)
        raster_to_mosiac.append(raster)

    if verbose:
        print("Merging rasters...")
    arr, transform = merge(raster_to_mosiac, **merge_args)

    output_meta = raster.meta.copy()
    output_meta.update(
        {
            "driver": "GTiff",
            "height": arr.shape[1],
            "width": arr.shape[2],
            "transform": transform,
        }
    )

    with rio.open(output, "w", **output_meta) as m:
        m.write(arr)

    if to_cog:
        if verbose:
            print("Converting to COG...")
        image_to_cog(output, output)

    if verbose:
        print(f"Saved mosaic to {output}")


def mosaic_opera(DS, merge_args={}):
    """Mosaics a list of OPERA product granules into a single image (in memory).

    Args:
        DS (list): A list of OPERA product granules opened as xarray.DataArray objects.
        merge_args (dict, optional): A dictionary of arguments to pass to the rioxarray.merge_arrays function. Defaults to {}.

    Returns:
        An xarray.DataArray containing the mosaic of the individual OPERA product granule DataArrays.
        A colormap for the mosaic, if in the original OPERA metadata, otherwise None.
        The nodata value for the mosaic corresponding to the original OPERA product granule metadata.
    """
    from rioxarray.merge import merge_arrays

    DA = []
    for ds in DS:
        nodata = ds.rio.nodata
        da = ds.fillna(nodata)
        DA.append(da)

    merged_arr = merge_arrays(DA)

    try:
        colormap = get_image_colormap(DS[0])
    except Exception as e:
        colormap = None
    return merged_arr, colormap, nodata


def geometry_bounds(geometry, decimals=4):
    """Returns the bounds of a geometry.

    Args:
        geometry (dict): A GeoJSON geometry.
        decimals (int, optional): The number of decimal places to round the bounds to. Defaults to 4.

    Returns:
        A list of bounds in the form of [minx, miny, maxx, maxy].
    """
    if isinstance(geometry, dict):
        if "geometry" in geometry:
            coords = geometry["geometry"]["coordinates"][0]
        else:
            coords = geometry["coordinates"][0]

    else:
        raise ValueError("geometry must be a GeoJSON-like dictionary.")

    x = [p[0] for p in coords]
    y = [p[1] for p in coords]
    west = round(min(x), decimals)
    east = round(max(x), decimals)
    south = round(min(y), decimals)
    north = round(max(y), decimals)
    return [west, south, east, north]


def reproject(
    image, output, dst_crs="EPSG:4326", resampling="nearest", to_cog=True, **kwargs: Any
) -> None:
    """Reprojects an image.

    Args:
        image (str): The input image filepath.
        output (str): The output image filepath.
        dst_crs (str, optional): The destination CRS. Defaults to "EPSG:4326".
        resampling (Resampling, optional): The resampling method. Defaults to "nearest".
        to_cog (bool, optional): Whether to convert the output image to a Cloud Optimized GeoTIFF. Defaults to True.
        **kwargs: Additional keyword arguments to pass to rasterio.open.

    """
    import rasterio as rio
    from rasterio.warp import Resampling, calculate_default_transform, reproject

    if isinstance(resampling, str):
        resampling = getattr(Resampling, resampling)

    image = os.path.abspath(image)
    output = os.path.abspath(output)

    if not os.path.exists(os.path.dirname(output)):
        os.makedirs(os.path.dirname(output))

    with rio.open(image, **kwargs) as src:
        transform, width, height = calculate_default_transform(
            src.crs, dst_crs, src.width, src.height, *src.bounds
        )
        kwargs = src.meta.copy()
        kwargs.update(
            {
                "crs": dst_crs,
                "transform": transform,
                "width": width,
                "height": height,
            }
        )

        with rio.open(output, "w", **kwargs) as dst:
            for i in range(1, src.count + 1):
                reproject(
                    source=rio.band(src, i),
                    destination=rio.band(dst, i),
                    src_transform=src.transform,
                    src_crs=src.crs,
                    dst_transform=transform,
                    dst_crs=dst_crs,
                    resampling=resampling,
                    **kwargs,
                )

    if to_cog:
        image_to_cog(output, output)


def image_check(image):
    from localtileserver import TileClient

    if isinstance(image, str):
        if image.startswith("http") or os.path.exists(image):
            pass
        else:
            raise ValueError("image must be a URL or filepath.")
    elif isinstance(image, TileClient):
        pass
    else:
        raise ValueError("image must be a URL or filepath.")


def image_client(image, **kwargs: Any):
    """Get a LocalTileserver TileClient from an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        A LocalTileserver TileClient.
    """
    image_check(image)

    _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    return client


def image_center(image, **kwargs: Any):
    """Get the center of an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        A tuple of (latitude, longitude).
    """
    image_check(image)

    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image
    return client.center()


def image_bounds(image, **kwargs: Any):
    """Get the bounds of an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        A list of bounds in the form of [(south, west), (north, east)].
    """

    image_check(image)
    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image
    bounds = client.bounds()
    return [(bounds[0], bounds[2]), (bounds[1], bounds[3])]


def image_metadata(image, **kwargs: Any):
    """Get the metadata of an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        A dictionary of image metadata.
    """
    image_check(image)

    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image
    return client.metadata


def image_bandcount(image, **kwargs: Any):
    """Get the number of bands in an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        The number of bands in the image.
    """

    image_check(image)

    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image
    return len(client.metadata()["bands"])


def image_size(image, **kwargs: Any):
    """Get the size (width, height) of an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        A tuple of (width, height).
    """
    image_check(image)

    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image

    metadata = client.metadata()
    return metadata["sourceSizeX"], metadata["sourceSizeY"]


def image_projection(image, **kwargs: Any):
    """Get the projection of an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        The projection of the image.
    """
    image_check(image)

    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image
    return client.metadata()["Projection"]


def image_set_crs(image, epsg):
    """Define the CRS of an image.

    Args:
        image (str): The input image filepath
        epsg (int): The EPSG code of the CRS to set.
    """

    import rasterio
    from rasterio.crs import CRS

    with rasterio.open(image, "r+") as rds:
        rds.crs = CRS.from_epsg(epsg)


def image_geotransform(image, **kwargs: Any):
    """Get the geotransform of an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        A list of geotransform values.
    """
    image_check(image)

    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image
    return client.metadata()["GeoTransform"]


def image_resolution(image, **kwargs: Any):
    """Get the resolution of an image.

    Args:
        image (str): The input image filepath or URL.

    Returns:
        The resolution of the image.
    """
    image_check(image)

    if isinstance(image, str):
        _, client = get_local_tile_layer(image, return_client=True, **kwargs)
    else:
        client = image
    return client.metadata()["GeoTransform"][1]


def image_bbox(raster_path, output_file=None, to_crs=None, **kwargs: Any):
    """Get the bounding box of an image.

    Args:
        raster_path (str): The input image filepath or URL.
        output_file (str, optional): The output file path. Defaults to None.
        to_crs (str, optional): The CRS to convert the bounding box to. Defaults to None.
    """

    import geopandas as gpd
    import rasterio
    from shapely.geometry import box

    with rasterio.open(raster_path) as src:
        bounds = src.bounds
        bbox = box(bounds.left, bounds.bottom, bounds.right, bounds.top)
        crs = src.crs

    # Convert to GeoDataFrame
    gdf = gpd.GeoDataFrame({"geometry": [bbox]}, crs=crs)
    if to_crs is not None:
        gdf = gdf.to_crs(to_crs)

    # Save to file (e.g., GeoJSON)
    if output_file is not None:
        gdf.to_file(output_file, driver="GeoJSON")
    else:
        return gdf


def find_files(
    input_dir, ext=None, fullpath=True, recursive=True, include_hidden=False
):
    """Find files in a directory.

    Args:
        input_dir (str): The input directory.
        ext (str, optional): The file extension to match. Defaults to None.
        fullpath (bool, optional): Whether to return the full path. Defaults to True.
        recursive (bool, optional): Whether to search recursively. Defaults to True.
        include_hidden (bool, optional): Whether to include hidden files. Defaults to False.
    Returns:
        A list of matching files.
    """

    from pathlib import Path

    files = []

    if ext is None:
        ext = "*"
    else:
        ext = ext.replace(".", "")

    ext = f"*.{ext}"

    if recursive:
        if fullpath:
            files = [str(path.joinpath()) for path in Path(input_dir).rglob(ext)]
        else:
            files = [str(path.name) for path in Path(input_dir).rglob(ext)]
    else:
        if fullpath:
            files = [str(path.joinpath()) for path in Path(input_dir).glob(ext)]
        else:
            files = [path.name for path in Path(input_dir).glob(ext)]

    files.sort()
    if not include_hidden:
        files = [file for file in files if not os.path.basename(file).startswith(".")]
    return files


def sort_files(
    files: List[str], names: Optional[List[str]] = None, fill_na=True
) -> List[str]:
    """
    Sorts a list of files based on a list of names.

    If names is None, the function sorts the list of files in-place and returns the sorted list.
    If names is provided, the function filters the list of files to include only those that
        contain any of the names in the list, and returns the filtered list.

    Args:
        files (List[str]): The list of files to sort or filter.
        names (Optional[List[str]], optional): The list of names to filter by. Defaults to None.

    Returns:
        The sorted or filtered list of files.
    """
    if names is None:

        return sorted(files)
    else:
        filenames = []
        for index, name in enumerate(names):
            for file in files:
                if name in os.path.basename(file):
                    filenames.append(file)
                    break
            if fill_na and index >= len(filenames):
                filenames.append(None)

        return filenames


def zoom_level_resolution(zoom, latitude=0):
    """Returns the approximate pixel scale based on zoom level and latutude.
        See https://blogs.bing.com/maps/2006/02/25/map-control-zoom-levels-gt-resolution

    Args:
        zoom (int): The zoom level.
        latitude (float, optional): The latitude. Defaults to 0.

    Returns:
        Map resolution in meters.
    """
    import math

    resolution = 156543.04 * math.cos(latitude) / math.pow(2, zoom)
    return abs(resolution)


def lnglat_to_meters(longitude, latitude):
    """coordinate conversion between lat/lon in decimal degrees to web mercator

    Args:
        longitude (float): The longitude.
        latitude (float): The latitude.

    Returns:
        A tuple of (x, y) in meters.
    """
    import numpy as np

    origin_shift = np.pi * 6378137
    easting = longitude * origin_shift / 180.0
    northing = np.log(np.tan((90 + latitude) * np.pi / 360.0)) * origin_shift / np.pi

    if np.isnan(easting):
        if longitude > 0:
            easting = 20026376
        else:
            easting = -20026376

    if np.isnan(northing):
        if latitude > 0:
            northing = 20048966
        else:
            northing = -20048966

    return (easting, northing)


def meters_to_lnglat(x, y):
    """coordinate conversion between web mercator to lat/lon in decimal degrees

    Args:
        x (float): The x coordinate.
        y (float): The y coordinate.

    Returns:
        A tuple of (longitude, latitude) in decimal degrees.
    """

    origin_shift = np.pi * 6378137
    longitude = (x / origin_shift) * 180.0
    latitude = (y / origin_shift) * 180.0
    latitude = (
        180 / np.pi * (2 * np.arctan(np.exp(latitude * np.pi / 180.0)) - np.pi / 2.0)
    )
    return (longitude, latitude)


def bounds_to_xy_range(
    bounds: Union[
        List[Union[Tuple[float, float], float]], Tuple[float, float, float, float]
    ],
) -> Tuple[Tuple[float, float], Tuple[float, float]]:
    """
    Convert bounds to x and y range to be used as input to bokeh map.

    Args:
        bounds (Union[List[Union[Tuple[float, float], float]], Tuple[float, float, float, float]]):
            A list of bounds in the form [(south, west), (north, east)] or [xmin, ymin, xmax, ymax].

    Returns:
        Tuple[Tuple[float, float], Tuple[float, float]]: A tuple of (x_range, y_range).
    """
    if isinstance(bounds, tuple):
        if len(bounds) != 4:
            raise ValueError(
                "Tuple bounds must have exactly 4 elements (xmin, ymin, xmax, ymax)."
            )
        west, south, east, north = bounds
    elif isinstance(bounds, list):
        if len(bounds) == 2 and all(
            isinstance(coord, tuple) and len(coord) == 2 for coord in bounds
        ):
            (south, west), (north, east) = bounds
        elif len(bounds) == 4 and all(
            isinstance(coord, (int, float)) for coord in bounds
        ):
            west, south, east, north = bounds
        else:
            raise ValueError(
                "List bounds must be in the form [(south, west), (north, east)] or [xmin, ymin, xmax, ymax]."
            )
    else:
        raise TypeError("bounds must be a list or tuple")

    xmin, ymin = lnglat_to_meters(west, south)
    xmax, ymax = lnglat_to_meters(east, north)
    x_range = (xmin, xmax)
    y_range = (ymin, ymax)
    return x_range, y_range


def center_zoom_to_xy_range(
    center, zoom
) -> Tuple[Tuple[float, float], Tuple[float, float]]:
    """Convert center and zoom to x and y range to be used as input to bokeh map.

    Args:
        center (tuple): A tuple of (latitude, longitude).
        zoom (int): The zoom level.

    Returns:
        A tuple of (x_range, y_range).
    """

    if isinstance(center, tuple) or isinstance(center, list):
        pass
    else:
        raise TypeError("center must be a tuple or list")

    if not isinstance(zoom, int):
        raise TypeError("zoom must be an integer")

    latitude, longitude = center
    x_range = (-179, 179)
    y_range = (-70, 70)
    x_full_length = x_range[1] - x_range[0]
    y_full_length = y_range[1] - y_range[0]

    x_length = x_full_length / 2 ** (zoom - 2)
    y_length = y_full_length / 2 ** (zoom - 2)

    south = latitude - y_length / 2
    north = latitude + y_length / 2
    west = longitude - x_length / 2
    east = longitude + x_length / 2

    xmin, ymin = lnglat_to_meters(west, south)
    xmax, ymax = lnglat_to_meters(east, north)

    x_range = (xmin, xmax)
    y_range = (ymin, ymax)

    return x_range, y_range


def get_geometry_coords(row, geom, coord_type, shape_type, mercator=False):
    """
    Returns the coordinates ('x' or 'y') of edges of a Polygon exterior.

    :param: (GeoPandas Series) row : The row of each of the GeoPandas DataFrame.
    :param: (str) geom : The column name.
    :param: (str) coord_type : Whether it's 'x' or 'y' coordinate.
    :param: (str) shape_type
    """

    # Parse the exterior of the coordinate
    if shape_type.lower() in ["polygon", "multipolygon"]:
        exterior = row[geom].geoms[0].exterior
        if coord_type == "x":
            # Get the x coordinates of the exterior
            coords = list(exterior.coords.xy[0])
            if mercator:
                coords = [lnglat_to_meters(x, 0)[0] for x in coords]
            return coords

        elif coord_type == "y":
            # Get the y coordinates of the exterior
            coords = list(exterior.coords.xy[1])
            if mercator:
                coords = [lnglat_to_meters(0, y)[1] for y in coords]
            return coords

    elif shape_type.lower() in ["linestring", "multilinestring"]:
        if coord_type == "x":
            coords = list(row[geom].coords.xy[0])
            if mercator:
                coords = [lnglat_to_meters(x, 0)[0] for x in coords]
            return coords
        elif coord_type == "y":
            coords = list(row[geom].coords.xy[1])
            if mercator:
                coords = [lnglat_to_meters(0, y)[1] for y in coords]
            return coords

    elif shape_type.lower() in ["point", "multipoint"]:
        exterior = row[geom]

        if coord_type == "x":
            # Get the x coordinates of the exterior
            coords = exterior.coords.xy[0][0]
            if mercator:
                coords = lnglat_to_meters(coords, 0)[0]
            return coords

        elif coord_type == "y":
            # Get the y coordinates of the exterior
            coords = exterior.coords.xy[1][0]
            if mercator:
                coords = lnglat_to_meters(0, coords)[1]
            return coords


def gdf_to_bokeh(gdf):
    """
    Function to convert a GeoPandas GeoDataFrame to a Bokeh
    ColumnDataSource object.

    :param: (GeoDataFrame) gdf: GeoPandas GeoDataFrame with polygon(s) under
                                the column name 'geometry.'

    :return: ColumnDataSource for Bokeh.
    """
    from bokeh.plotting import ColumnDataSource

    shape_type = gdf_geom_type(gdf)

    gdf_new = gdf.drop("geometry", axis=1).copy()
    gdf_new["x"] = gdf.apply(
        get_geometry_coords,
        geom="geometry",
        coord_type="x",
        shape_type=shape_type,
        mercator=True,
        axis=1,
    )

    gdf_new["y"] = gdf.apply(
        get_geometry_coords,
        geom="geometry",
        coord_type="y",
        shape_type=shape_type,
        mercator=True,
        axis=1,
    )

    return ColumnDataSource(gdf_new)


def get_overlap(img1, img2, overlap, out_img1=None, out_img2=None, to_cog=True):
    """Get overlapping area of two images.

    Args:
        img1 (str): Path to the first image.
        img2 (str): Path to the second image.
        overlap (str): Path to the output overlap area in GeoJSON format.
        out_img1 (str, optional): Path to the cropped image of the first image.
        out_img2 (str, optional): Path to the cropped image of the second image.
        to_cog (bool, optional): Whether to convert the output images to COG.

    Returns:
        Path to the overlap area in GeoJSON format.
    """
    import json

    import geopandas as gpd
    from osgeo import gdal, ogr, osr

    extent = gdal.Info(img1, format="json")["wgs84Extent"]
    poly1 = ogr.CreateGeometryFromJson(json.dumps(extent))
    extent = gdal.Info(img2, format="json")["wgs84Extent"]
    poly2 = ogr.CreateGeometryFromJson(json.dumps(extent))
    intersection = poly1.Intersection(poly2)
    gg = gdal.OpenEx(intersection.ExportToJson())
    ds = gdal.VectorTranslate(
        overlap,
        srcDS=gg,
        format="GeoJSON",
        layerCreationOptions=["RFC7946=YES", "WRITE_BBOX=YES"],
    )
    ds = None

    d = gdal.Open(img1)
    proj = osr.SpatialReference(wkt=d.GetProjection())
    epsg = proj.GetAttrValue("AUTHORITY", 1)

    gdf = gpd.read_file(overlap)
    gdf.to_crs(epsg=epsg, inplace=True)
    gdf.to_file(overlap)

    if out_img1 is not None:
        clip_image(img1, overlap, out_img1, to_cog=to_cog)

    if out_img2 is not None:
        clip_image(img2, overlap, out_img2, to_cog=to_cog)

    return overlap


def add_mask_to_image(image, mask, output, color="red"):
    """Overlay a binary mask (e.g., roads, building footprints, etc) on an image. Credits to Xingjian Shi for the sample code.

    Args:
        image (str): A local path or HTTP URL to an image.
        mask (str): A local path or HTTP URL to a binary mask.
        output (str): A local path to the output image.
        color (str, optional): Color of the mask. Defaults to 'red'.

    Raises:
        ImportError: If rasterio and detectron2 are not installed.
    """
    try:
        import rasterio
        from detectron2.utils.visualizer import Visualizer
        from PIL import Image
    except ImportError:
        raise ImportError(
            "Please install rasterio and detectron2 to use this function. See https://detectron2.readthedocs.io/en/latest/tutorials/install.html"
        )

    ds = rasterio.open(image)
    image_arr = ds.read()

    mask_arr = rasterio.open(mask).read()

    vis = Visualizer(image_arr.transpose((1, 2, 0)))
    vis.draw_binary_mask(mask_arr[0] > 0, color=color)

    out_arr = Image.fromarray(vis.get_output().get_image())

    out_arr.save(output)

    if ds.crs is not None:
        numpy_to_cog(output, output, profile=image)


def is_on_aws():
    """Check if the current notebook is running on AWS.

    Returns:
        True if the notebook is running on AWS.
    """

    try:
        import psutil
    except ImportError:
        install_package("psutil")
        import psutil

    on_aws = False
    try:
        output = psutil.Process().parent().cmdline()

        for item in output:
            if item.endswith(".aws") or "ec2-user" in item:
                on_aws = True
    except:
        pass
    return on_aws


def is_studio_lab():
    """Check if the current notebook is running on Studio Lab.

    Returns:
        True if the notebook is running on Studio Lab.
    """

    try:
        import psutil
    except ImportError:
        install_package("psutil")
        import psutil

    on_studio_lab = False

    try:
        output = psutil.Process().parent().cmdline()

        for item in output:
            if "studiolab/bin" in item:
                on_studio_lab = True
    except:
        pass
    return on_studio_lab


def bbox_to_gdf(bbox, crs="epsg:4326") -> "gpd.GeoDataFrame":
    """Convert a bounding box to a GeoPandas GeoDataFrame.

    Args:
        bbox (list): A bounding box in the format of [minx, miny, maxx, maxy].
        crs (str, optional): The CRS of the bounding box. Defaults to 'epsg:4326'.

    Returns:
        A GeoDataFrame with a single polygon.
    """
    import geopandas as gpd
    from shapely.geometry import Polygon

    return gpd.GeoDataFrame(
        geometry=[Polygon.from_bounds(*bbox)],
        crs=crs,
    )


def bbox_to_polygon(bbox) -> Any:
    """Convert a bounding box to a shapely Polygon.

    Args:
        bbox (list): A bounding box in the format of [minx, miny, maxx, maxy].

    Returns:
        A shapely Polygon.
    """
    from shapely.geometry import Polygon

    return Polygon.from_bounds(*bbox)


def vector_area(vector, unit="m2", crs="epsg:3857"):
    """Calculate the area of a vector.

    Args:
        vector (str): A local path or HTTP URL to a vector.
        unit (str, optional): The unit of the area, can be 'm2', 'km2', 'ha', or 'acres'. Defaults to 'm2'.

    Returns:
        The area of the vector.
    """
    import geopandas as gpd

    if isinstance(vector, str):
        gdf = gpd.read_file(vector)
    elif isinstance(vector, gpd.GeoDataFrame):
        gdf = vector
    else:
        gdf = None
        raise ValueError("Invalid input vector.")

    area = gdf.to_crs(crs).area.sum()

    if unit == "m2":
        return area
    elif unit == "km2":
        return area / 1000000
    elif unit == "ha":
        return area / 10000
    elif unit == "acres":
        return area / 4046.8564224
    else:
        raise ValueError("Invalid unit.")


def image_filesize(
    region,
    cellsize,
    bands=1,
    dtype="uint8",
    unit="MB",
    source_crs="epsg:4326",
    dst_crs="epsg:3857",
    bbox=False,
):
    """Calculate the size of an image in a given region and cell size.

    Args:
        region (list): A bounding box in the format of [minx, miny, maxx, maxy].
        cellsize (float): The resolution of the image.
        bands (int, optional): Number of bands. Defaults to 1.
        dtype (str, optional): Data type, such as unit8, float32. For more info,
            see https://numpy.org/doc/stable/user/basics.types.html. Defaults to 'uint8'.
        unit (str, optional): The unit of the output. Defaults to 'MB'.
        source_crs (str, optional): The CRS of the region. Defaults to 'epsg:4326'.
        dst_crs (str, optional): The destination CRS to calculate the area. Defaults to 'epsg:3857'.
        bbox (bool, optional): Whether to use the bounding box of the region to calculate the area. Defaults to False.

    Returns:
        The size of the image in a given unit.
    """
    import geopandas as gpd
    import numpy as np

    if bbox:
        if isinstance(region, gpd.GeoDataFrame):
            region = region.to_crs(dst_crs).total_bounds.tolist()
        elif isinstance(region, str) and os.path.exists(region):
            region = gpd.read_file(region).to_crs(dst_crs).total_bounds.tolist()
        elif isinstance(region, list):
            region = (
                bbox_to_gdf(region, crs=source_crs)
                .to_crs(dst_crs)
                .total_bounds.tolist()
            )
        else:
            raise ValueError("Invalid input region.")

        bytes = (
            np.prod(
                [
                    int((region[2] - region[0]) / cellsize),
                    int((region[3] - region[1]) / cellsize),
                    bands,
                ]
            )
            * np.dtype(dtype).itemsize
        )
    else:
        if isinstance(region, list):
            region = bbox_to_gdf(region, crs=source_crs)

        bytes = (
            vector_area(region, crs=dst_crs)
            / pow(cellsize, 2)
            * np.dtype(dtype).itemsize
            * bands
        )

    unit = unit.upper()

    if unit == "KB":
        return bytes / 1024
    elif unit == "MB":
        return bytes / pow(1024, 2)
    elif unit == "GB":
        return bytes / pow(1024, 3)
    elif unit == "TB":
        return bytes / pow(1024, 4)
    elif unit == "PB":
        return bytes / pow(1024, 5)
    else:
        return bytes


def is_jupyterlite():
    """Check if the current notebook is running on JupyterLite.

    Returns:
        True if the notebook is running on JupyterLite.
    """
    import sys

    if "pyodide" in sys.modules:
        return True
    else:
        return False


async def download_file_lite(
    url, output=None, binary=False, overwrite=False, **kwargs: Any
):
    """Download a file using Pyodide. This function is only available on JupyterLite. Call the function with await, such as await download_file_lite(url).

    Args:
        url (str): The URL of the file.
        output (str, optional): The local path to save the file. Defaults to None.
        binary (bool, optional): Whether the file is binary. Defaults to False.
        overwrite (bool, optional): Whether to overwrite the file if it exists. Defaults to False.
    """
    import sys

    import pyodide  # pylint: disable=E0401

    if "pyodide" not in sys.modules:
        raise ValueError("Pyodide is not available.")

    if output is None:
        output = os.path.basename(url)

    output = os.path.abspath(output)

    ext = os.path.splitext(output)[1]

    if ext in [".png", "jpg", ".tif", ".tiff", "zip", "gz", "bz2", "xz"]:
        binary = True

    if os.path.exists(output) and not overwrite:
        print(f"{output} already exists, skip downloading.")
        return output

    if binary:
        response = await pyodide.http.pyfetch(url)
        with open(output, "wb") as f:
            f.write(await response.bytes())

    else:
        obj = pyodide.http.open_url(url)
        with open(output, "w") as fd:
            shutil.copyfileobj(obj, fd)

    return output


def create_legend(
    title="Legend",
    labels=None,
    colors=None,
    legend_dict=None,
    builtin_legend=None,
    opacity=1.0,
    position="bottomright",
    draggable=True,
    output=None,
    style={},
    shape_type="rectangle",
) -> str:
    """Create a legend in HTML format. Reference: https://bit.ly/3oV6vnH

    Args:
        title (str, optional): Title of the legend. Defaults to 'Legend'. Defaults to "Legend".
        colors (list, optional): A list of legend colors. Defaults to None.
        labels (list, optional): A list of legend labels. Defaults to None.
        legend_dict (dict, optional): A dictionary containing legend items as keys and color as values.
            If provided, legend_keys and legend_colors will be ignored. Defaults to None.
        builtin_legend (str, optional): Name of the builtin legend to add to the map. Defaults to None.
        opacity (float, optional): The opacity of the legend. Defaults to 1.0.
        position (str, optional): The position of the legend, can be one of the following:
            "topleft", "topright", "bottomleft", "bottomright". Defaults to "bottomright".
        draggable (bool, optional): If True, the legend can be dragged to a new position. Defaults to True.
        output (str, optional): The output file path (*.html) to save the legend. Defaults to None.
        style (dict): Additional keyword arguments to style the legend, such as position, bottom, right, z-index,
            border, background-color, border-radius, padding, font-size, etc. The default style is:
            style = {
                'position': 'fixed',
                'z-index': '9999',
                'border': '2px solid grey',
                'background-color': 'rgba(255, 255, 255, 0.8)',
                'border-radius': '5px',
                'padding': '10px',
                'font-size': '14px',
                'bottom': '20px',
                'right': '5px'
            }

    Returns:
        The HTML code of the legend.
    """

    import importlib.resources

    from .legends import builtin_legends

    pkg_dir = os.path.dirname(importlib.resources.files("leafmap") / "leafmap.py")
    legend_template = os.path.join(pkg_dir, "data/template/legend_style.html")

    if draggable:
        legend_template = os.path.join(pkg_dir, "data/template/legend.txt")

    if not os.path.exists(legend_template):
        raise FileNotFoundError("The legend template does not exist.")

    if labels is not None:
        if not isinstance(labels, list):
            print("The legend keys must be a list.")
            return
    else:
        labels = ["One", "Two", "Three", "Four", "etc"]

    if colors is not None:
        if not isinstance(colors, list):
            print("The legend colors must be a list.")
            return
        elif all(isinstance(item, tuple) for item in colors):
            try:
                colors = [rgb_to_hex(x) for x in colors]
            except Exception as e:
                print(e)
        elif all((item.startswith("#") and len(item) == 7) for item in colors):
            pass
        elif all((len(item) == 6) for item in colors):
            pass
        else:
            print("The legend colors must be a list of tuples.")
            return
    else:
        colors = [
            "#8DD3C7",
            "#FFFFB3",
            "#BEBADA",
            "#FB8072",
            "#80B1D3",
        ]

    if len(labels) != len(colors):
        print("The legend keys and values must be the same length.")
        return

    allowed_builtin_legends = builtin_legends.keys()
    if builtin_legend is not None:
        if builtin_legend not in allowed_builtin_legends:
            print(
                "The builtin legend must be one of the following: {}".format(
                    ", ".join(allowed_builtin_legends)
                )
            )
            return
        else:
            legend_dict = builtin_legends[builtin_legend]
            labels = list(legend_dict.keys())
            colors = list(legend_dict.values())

    if legend_dict is not None:
        if not isinstance(legend_dict, dict):
            print("The legend dict must be a dictionary.")
            return
        else:
            labels = list(legend_dict.keys())
            colors = list(legend_dict.values())
            if all(isinstance(item, tuple) for item in colors):
                try:
                    colors = [rgb_to_hex(x) for x in colors]
                except Exception as e:
                    print(e)

    allowed_positions = [
        "topleft",
        "topright",
        "bottomleft",
        "bottomright",
    ]
    if position not in allowed_positions:
        raise ValueError(
            "The position must be one of the following: {}".format(
                ", ".join(allowed_positions)
            )
        )

    if position == "bottomright":
        if "bottom" not in style:
            style["bottom"] = "20px"
        if "right" not in style:
            style["right"] = "5px"
        if "left" in style:
            del style["left"]
        if "top" in style:
            del style["top"]
    elif position == "bottomleft":
        if "bottom" not in style:
            style["bottom"] = "5px"
        if "left" not in style:
            style["left"] = "5px"
        if "right" in style:
            del style["right"]
        if "top" in style:
            del style["top"]
    elif position == "topright":
        if "top" not in style:
            style["top"] = "5px"
        if "right" not in style:
            style["right"] = "5px"
        if "left" in style:
            del style["left"]
        if "bottom" in style:
            del style["bottom"]
    elif position == "topleft":
        if "top" not in style:
            style["top"] = "5px"
        if "left" not in style:
            style["left"] = "5px"
        if "right" in style:
            del style["right"]
        if "bottom" in style:
            del style["bottom"]

    if "position" not in style:
        style["position"] = "fixed"
    if "z-index" not in style:
        style["z-index"] = "9999"
    if "background-color" not in style:
        style["background-color"] = "rgba(255, 255, 255, 0.8)"
    if "padding" not in style:
        style["padding"] = "10px"
    if "border-radius" not in style:
        style["border-radius"] = "5px"
    if "font-size" not in style:
        style["font-size"] = "14px"

    content = []

    with open(legend_template) as f:
        lines = f.readlines()

    if draggable:
        for index, line in enumerate(lines):
            if index < 36:
                content.append(line)
            elif index == 36:
                line = lines[index].replace("Legend", title)
                content.append(line)
            elif index < 39:
                content.append(line)
            elif index == 39:
                for i, color in enumerate(colors):
                    item = f"    <li><span style='background:{check_color(color)};opacity:{opacity};'></span>{labels[i]}</li>\n"
                    content.append(item)
            elif index > 41:
                content.append(line)
        content = content[3:-1]

    else:
        for index, line in enumerate(lines):
            if index < 8:
                content.append(line)
            elif index == 8:
                for key, value in style.items():
                    content.append(
                        "              {}: {};\n".format(key.replace("_", "-"), value)
                    )
            elif index < 17:
                pass
            elif index < 19:
                content.append(line)
            elif index == 19:
                content.append(line.replace("Legend", title))
            elif index < 22:
                content.append(line)
            elif index == 22:
                for index, key in enumerate(labels):
                    color = colors[index]
                    if not color.startswith("#"):
                        color = "#" + color
                    item = "                    <li><span style='background:{};opacity:{};'></span>{}</li>\n".format(
                        color, opacity, key
                    )
                    content.append(item)
            elif index < 33:
                pass
            else:
                content.append(line)

    legend_text = "".join(content)
    if shape_type == "circle":
        legend_text = legend_text.replace("width: 30px", "width: 16px")
        legend_text = legend_text.replace(
            "border: 1px solid #999;",
            "border-radius: 50%;\n      border: 1px solid #999;",
        )
    elif shape_type == "line":
        legend_text = legend_text.replace("height: 16px", "height: 3px")

    if output is not None:
        with open(output, "w") as f:
            f.write(legend_text)
    else:
        return legend_text


def png_to_gif(in_dir, out_gif, fps=10, loop=0):
    """Convert a list of png images to gif.

    Args:
        in_dir (str): The input directory containing png images.
        out_gif (str): The output file path to the gif.
        fps (int, optional): Frames per second. Defaults to 10.
        loop (bool, optional): controls how many times the animation repeats. 1 means that the animation will play once and then stop (displaying the last frame). A value of 0 means that the animation will repeat forever. Defaults to 0.

    Raises:
        FileNotFoundError: No png images could be found.
    """
    import glob

    from PIL import Image

    if not out_gif.endswith(".gif"):
        raise ValueError("The out_gif must be a gif file.")

    out_gif = os.path.abspath(out_gif)

    out_dir = os.path.dirname(out_gif)
    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    # Create the frames
    frames = []
    imgs = list(glob.glob(os.path.join(in_dir, "*.png")))
    imgs.sort()

    if len(imgs) == 0:
        raise FileNotFoundError(f"No png could be found in {in_dir}.")

    for i in imgs:
        new_frame = Image.open(i)
        frames.append(new_frame)

    # Save into a GIF file that loops forever
    frames[0].save(
        out_gif,
        format="GIF",
        append_images=frames[1:],
        save_all=True,
        duration=1000 / fps,
        loop=loop,
    )


def add_text_to_gif(
    in_gif,
    out_gif,
    xy=None,
    text_sequence=None,
    font_type="arial.ttf",
    font_size=20,
    font_color="#000000",
    add_progress_bar=True,
    progress_bar_color="white",
    progress_bar_height=5,
    duration=100,
    loop=0,
):
    """Adds animated text to a GIF image.

    Args:
        in_gif (str): The file path to the input GIF image.
        out_gif (str): The file path to the output GIF image.
        xy (tuple, optional): Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        text_sequence (int, str, list, optional): Text to be drawn. It can be an integer number, a string, or a list of strings. Defaults to None.
        font_type (str, optional): Font type. Defaults to "arial.ttf".
        font_size (int, optional): Font size. Defaults to 20.
        font_color (str, optional): Font color. It can be a string (e.g., 'red'), rgb tuple (e.g., (255, 127, 0)), or hex code (e.g., '#ff00ff').  Defaults to '#000000'.
        add_progress_bar (bool, optional): Whether to add a progress bar at the bottom of the GIF. Defaults to True.
        progress_bar_color (str, optional): Color for the progress bar. Defaults to 'white'.
        progress_bar_height (int, optional): Height of the progress bar. Defaults to 5.
        duration (int, optional): controls how long each frame will be displayed for, in milliseconds. It is the inverse of the frame rate. Setting it to 100 milliseconds gives 10 frames per second. You can decrease the duration to give a smoother animation.. Defaults to 100.
        loop (int, optional): controls how many times the animation repeats. The default, 1, means that the animation will play once and then stop (displaying the last frame). A value of 0 means that the animation will repeat forever. Defaults to 0.

    """
    import importlib.resources
    import io

    from PIL import Image, ImageDraw, ImageFont, ImageSequence

    warnings.simplefilter("ignore")
    pkg_dir = os.path.dirname(importlib.resources.files("leafmap") / "leafmap.py")
    default_font = os.path.join(pkg_dir, "data/fonts/arial.ttf")

    in_gif = os.path.abspath(in_gif)
    out_gif = os.path.abspath(out_gif)

    if not os.path.exists(in_gif):
        print("The input gif file does not exist.")
        return

    if not os.path.exists(os.path.dirname(out_gif)):
        os.makedirs(os.path.dirname(out_gif))

    if font_type == "arial.ttf":
        font = ImageFont.truetype(default_font, font_size)
    elif font_type == "alibaba.otf":
        default_font = os.path.join(pkg_dir, "data/fonts/alibaba.otf")
        font = ImageFont.truetype(default_font, font_size)
    else:
        try:
            font_list = system_fonts(show_full_path=True)
            font_names = [os.path.basename(f) for f in font_list]
            if (font_type in font_list) or (font_type in font_names):
                font = ImageFont.truetype(font_type, font_size)
            else:
                print(
                    "The specified font type could not be found on your system. Using the default font instead."
                )
                font = ImageFont.truetype(default_font, font_size)
        except Exception as e:
            print(e)
            font = ImageFont.truetype(default_font, font_size)

    color = check_color(font_color)
    progress_bar_color = check_color(progress_bar_color)

    try:
        image = Image.open(in_gif)
    except Exception as e:
        print("An error occurred while opening the gif.")
        print(e)
        return

    count = image.n_frames
    W, H = image.size
    progress_bar_widths = [i * 1.0 / count * W for i in range(1, count + 1)]
    progress_bar_shapes = [
        [(0, H - progress_bar_height), (x, H)] for x in progress_bar_widths
    ]

    if xy is None:
        # default text location is 5% width and 5% height of the image.
        xy = (int(0.05 * W), int(0.05 * H))
    elif (xy is not None) and (not isinstance(xy, tuple)) and (len(xy) == 2):
        print("xy must be a tuple, e.g., (10, 10), ('10%', '10%')")
        return
    elif all(isinstance(item, int) for item in xy) and (len(xy) == 2):
        x, y = xy
        if (x > 0) and (x < W) and (y > 0) and (y < H):
            pass
        else:
            print(
                f"xy is out of bounds. x must be within [0, {W}], and y must be within [0, {H}]"
            )
            return
    elif all(isinstance(item, str) for item in xy) and (len(xy) == 2):
        x, y = xy
        if ("%" in x) and ("%" in y):
            try:
                x = int(float(x.replace("%", "")) / 100.0 * W)
                y = int(float(y.replace("%", "")) / 100.0 * H)
                xy = (x, y)
            except Exception:
                raise Exception(
                    "The specified xy is invalid. It must be formatted like this ('10%', '10%')"
                )
    else:
        print(
            "The specified xy is invalid. It must be formatted like this: (10, 10) or ('10%', '10%')"
        )
        return

    if text_sequence is None:
        text = [str(x) for x in range(1, count + 1)]
    elif isinstance(text_sequence, int):
        text = [str(x) for x in range(text_sequence, text_sequence + count + 1)]
    elif isinstance(text_sequence, str):
        try:
            text_sequence = int(text_sequence)
            text = [str(x) for x in range(text_sequence, text_sequence + count + 1)]
        except Exception:
            text = [text_sequence] * count
    elif isinstance(text_sequence, list) and len(text_sequence) != count:
        print(
            f"The length of the text sequence must be equal to the number ({count}) of frames in the gif."
        )
        return
    else:
        text = [str(x) for x in text_sequence]

    try:
        frames = []
        # Loop over each frame in the animated image
        for index, frame in enumerate(ImageSequence.Iterator(image)):
            # Draw the text on the frame
            frame = frame.convert("RGB")
            draw = ImageDraw.Draw(frame)
            # w, h = draw.textsize(text[index])
            draw.text(xy, text[index], font=font, fill=color)
            if add_progress_bar:
                draw.rectangle(progress_bar_shapes[index], fill=progress_bar_color)
            del draw

            b = io.BytesIO()
            frame.save(b, format="GIF")
            frame = Image.open(b)

            frames.append(frame)
        # https://www.pythoninformer.com/python-libraries/pillow/creating-animated-gif/
        # Save the frames as a new image

        frames[0].save(
            out_gif,
            save_all=True,
            append_images=frames[1:],
            duration=duration,
            loop=loop,
            optimize=True,
        )
    except Exception as e:
        print(e)


def add_progress_bar_to_gif(
    in_gif,
    out_gif,
    progress_bar_color="blue",
    progress_bar_height=5,
    duration=100,
    loop=0,
):
    """Adds a progress bar to a GIF image.

    Args:
        in_gif (str): The file path to the input GIF image.
        out_gif (str): The file path to the output GIF image.
        progress_bar_color (str, optional): Color for the progress bar. Defaults to 'white'.
        progress_bar_height (int, optional): Height of the progress bar. Defaults to 5.
        duration (int, optional): controls how long each frame will be displayed for, in milliseconds. It is the inverse of the frame rate. Setting it to 100 milliseconds gives 10 frames per second. You can decrease the duration to give a smoother animation. Defaults to 100.
        loop (int, optional): controls how many times the animation repeats. The default, 1, means that the animation will play once and then stop (displaying the last frame). A value of 0 means that the animation will repeat forever. Defaults to 0.

    """
    import io

    from PIL import Image, ImageDraw, ImageSequence

    warnings.simplefilter("ignore")

    in_gif = os.path.abspath(in_gif)
    out_gif = os.path.abspath(out_gif)

    if not os.path.exists(in_gif):
        print("The input gif file does not exist.")
        return

    if not os.path.exists(os.path.dirname(out_gif)):
        os.makedirs(os.path.dirname(out_gif))

    progress_bar_color = check_color(progress_bar_color)

    try:
        image = Image.open(in_gif)
    except Exception as e:
        raise Exception("An error occurred while opening the gif.")

    count = image.n_frames
    W, H = image.size
    progress_bar_widths = [i * 1.0 / count * W for i in range(1, count + 1)]
    progress_bar_shapes = [
        [(0, H - progress_bar_height), (x, H)] for x in progress_bar_widths
    ]

    try:
        frames = []
        # Loop over each frame in the animated image
        for index, frame in enumerate(ImageSequence.Iterator(image)):
            # Draw the text on the frame
            frame = frame.convert("RGB")
            draw = ImageDraw.Draw(frame)
            # w, h = draw.textsize(text[index])
            draw.rectangle(progress_bar_shapes[index], fill=progress_bar_color)
            del draw

            b = io.BytesIO()
            frame.save(b, format="GIF")
            frame = Image.open(b)

            frames.append(frame)
        # https://www.pythoninformer.com/python-libraries/pillow/creating-animated-gif/
        # Save the frames as a new image

        frames[0].save(
            out_gif,
            save_all=True,
            append_images=frames[1:],
            duration=duration,
            loop=loop,
            optimize=True,
        )
    except Exception as e:
        raise Exception(e)


def add_image_to_gif(
    in_gif, out_gif, in_image, xy=None, image_size=(80, 80), circle_mask=False
):
    """Adds an image logo to a GIF image.

    Args:
        in_gif (str): Input file path to the GIF image.
        out_gif (str): Output file path to the GIF image.
        in_image (str): Input file path to the image.
        xy (tuple, optional): Top left corner of the text. It can be formatted like this: (10, 10) or ('15%', '25%'). Defaults to None.
        image_size (tuple, optional): Resize image. Defaults to (80, 80).
        circle_mask (bool, optional): Whether to apply a circle mask to the image. This only works with non-png images. Defaults to False.
    """
    import io

    from PIL import Image, ImageDraw, ImageSequence

    warnings.simplefilter("ignore")

    in_gif = os.path.abspath(in_gif)

    is_url = False
    if in_image.startswith("http"):
        is_url = True

    if not os.path.exists(in_gif):
        print("The input gif file does not exist.")
        return

    if (not is_url) and (not os.path.exists(in_image)):
        print("The provided logo file does not exist.")
        return

    out_dir = check_dir((os.path.dirname(out_gif)))
    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    try:
        gif = Image.open(in_gif)
    except Exception as e:
        print("An error occurred while opening the image.")
        print(e)
        return

    logo_raw_image = None
    try:
        if in_image.startswith("http"):
            logo_raw_image = open_image_from_url(in_image)
        else:
            in_image = os.path.abspath(in_image)
            logo_raw_image = Image.open(in_image)
    except Exception as e:
        print(e)

    logo_raw_size = logo_raw_image.size

    ratio = max(
        logo_raw_size[0] / image_size[0],
        logo_raw_size[1] / image_size[1],
    )
    image_resize = (int(logo_raw_size[0] / ratio), int(logo_raw_size[1] / ratio))
    image_size = min(logo_raw_size[0], image_size[0]), min(
        logo_raw_size[1], image_size[1]
    )

    logo_image = logo_raw_image.convert("RGBA")
    logo_image.thumbnail(image_size, Image.ANTIALIAS)

    gif_width, gif_height = gif.size
    mask_im = None

    if circle_mask:
        mask_im = Image.new("L", image_size, 0)
        draw = ImageDraw.Draw(mask_im)
        draw.ellipse((0, 0, image_size[0], image_size[1]), fill=255)

    if has_transparency(logo_raw_image):
        mask_im = logo_image.copy()

    if xy is None:
        # default logo location is 5% width and 5% height of the image.
        delta = 10
        xy = (gif_width - image_resize[0] - delta, gif_height - image_resize[1] - delta)
        # xy = (int(0.05 * gif_width), int(0.05 * gif_height))
    elif (xy is not None) and (not isinstance(xy, tuple)) and (len(xy) == 2):
        print("xy must be a tuple, e.g., (10, 10), ('10%', '10%')")
        return
    elif all(isinstance(item, int) for item in xy) and (len(xy) == 2):
        x, y = xy
        if (x > 0) and (x < gif_width) and (y > 0) and (y < gif_height):
            pass
        else:
            print(
                "xy is out of bounds. x must be within [0, {}], and y must be within [0, {}]".format(
                    gif_width, gif_height
                )
            )
            return
    elif all(isinstance(item, str) for item in xy) and (len(xy) == 2):
        x, y = xy
        if ("%" in x) and ("%" in y):
            try:
                x = int(float(x.replace("%", "")) / 100.0 * gif_width)
                y = int(float(y.replace("%", "")) / 100.0 * gif_height)
                xy = (x, y)
            except Exception:
                raise Exception(
                    "The specified xy is invalid. It must be formatted like this ('10%', '10%')"
                )

    else:
        raise Exception(
            "The specified xy is invalid. It must be formatted like this: (10, 10) or ('10%', '10%')"
        )

    try:
        frames = []
        for _, frame in enumerate(ImageSequence.Iterator(gif)):
            frame = frame.convert("RGBA")
            frame.paste(logo_image, xy, mask_im)

            b = io.BytesIO()
            frame.save(b, format="GIF")
            frame = Image.open(b)
            frames.append(frame)

        frames[0].save(out_gif, save_all=True, append_images=frames[1:])
    except Exception as e:
        print(e)


def reduce_gif_size(in_gif, out_gif=None):
    """Reduces a GIF image using ffmpeg.

    Args:
        in_gif (str): The input file path to the GIF image.
        out_gif (str, optional): The output file path to the GIF image. Defaults to None.
    """

    try:
        import ffmpeg
    except ImportError:
        print("ffmpeg is not installed on your computer. Skip reducing gif size.")
        return

    warnings.filterwarnings("ignore")

    if not is_tool("ffmpeg"):
        print("ffmpeg is not installed on your computer. Skip reducing gif size.")
        return

    if not os.path.exists(in_gif):
        print("The input gif file does not exist.")
        return

    if out_gif is None:
        out_gif = in_gif
    elif not os.path.exists(os.path.dirname(out_gif)):
        os.makedirs(os.path.dirname(out_gif))

    if in_gif == out_gif:
        tmp_gif = in_gif.replace(".gif", "_tmp.gif")
        shutil.copyfile(in_gif, tmp_gif)
        stream = ffmpeg.input(tmp_gif)
        stream = ffmpeg.output(stream, in_gif, loglevel="quiet").overwrite_output()
        ffmpeg.run(stream)
        os.remove(tmp_gif)

    else:
        stream = ffmpeg.input(in_gif)
        stream = ffmpeg.output(stream, out_gif, loglevel="quiet").overwrite_output()
        ffmpeg.run(stream)


def make_gif(images, out_gif, ext="jpg", fps=10, loop=0, mp4=False, clean_up=False):
    """Creates a gif from a list of images.

    Args:
        images (list | str): The list of images or input directory to create the gif from.
        out_gif (str): File path to the output gif.
        ext (str, optional): The extension of the images. Defaults to 'jpg'.
        fps (int, optional): The frames per second of the gif. Defaults to 10.
        loop (int, optional): The number of times to loop the gif. Defaults to 0.
        mp4 (bool, optional): Whether to convert the gif to mp4. Defaults to False.

    """
    import glob

    from PIL import Image

    ext = ext.replace(".", "")

    if isinstance(images, str) and os.path.isdir(images):
        images = list(glob.glob(os.path.join(images, f"*.{ext}")))
        if len(images) == 0:
            raise ValueError("No images found in the input directory.")
    elif not isinstance(images, list):
        raise ValueError("images must be a list or a path to the image directory.")

    images.sort()

    frames = [Image.open(image) for image in images]
    frame_one = frames[0]
    frame_one.save(
        out_gif,
        format="GIF",
        append_images=frames,
        save_all=True,
        duration=int(1000 / fps),
        loop=loop,
    )

    if mp4:
        if not is_tool("ffmpeg"):
            print("ffmpeg is not installed on your computer.")
            return

        if os.path.exists(out_gif):
            out_mp4 = out_gif.replace(".gif", ".mp4")
            cmd = f"ffmpeg -loglevel error -i {out_gif} -vcodec libx264 -crf 25 -pix_fmt yuv420p {out_mp4}"
            os.system(cmd)
            if not os.path.exists(out_mp4):
                raise Exception(f"Failed to create mp4 file.")
    if clean_up:
        for image in images:
            os.remove(image)


def create_timelapse(
    images: Union[List, str],
    out_gif: str,
    ext: str = ".tif",
    bands: Optional[List] = None,
    size: Optional[Tuple] = None,
    bbox: Optional[List] = None,
    fps: int = 5,
    loop: int = 0,
    add_progress_bar: bool = True,
    progress_bar_color: str = "blue",
    progress_bar_height: int = 5,
    add_text: bool = False,
    text_xy: Optional[Tuple] = None,
    text_sequence: Optional[List] = None,
    font_type: str = "arial.ttf",
    font_size: int = 20,
    font_color: str = "black",
    mp4: bool = False,
    quiet: bool = True,
    reduce_size: bool = False,
    clean_up: bool = True,
    **kwargs: Any,
):
    """Creates a timelapse gif from a list of images.

    Args:
        images (list | str): The list of images or input directory to create the gif from.
            For example, '/path/to/images/*.tif' or ['/path/to/image1.tif', '/path/to/image2.tif', ...]
        out_gif (str): File path to the output gif.
        ext (str, optional): The extension of the images. Defaults to '.tif'.
        bands (list, optional): The bands to use for the gif. For example, [0, 1, 2] for RGB, and [0] for grayscale. Defaults to None.
        size (tuple, optional): The size of the gif. For example, (500, 500). Defaults to None, using the original size.
        bbox (list, optional): The bounding box of the gif. For example, [xmin, ymin, xmax, ymax]. Defaults to None, using the original bounding box.
        fps (int, optional): The frames per second of the gif. Defaults to 5.
        loop (int, optional): The number of times to loop the gif. Defaults to 0, looping forever.
        add_progress_bar (bool, optional): Whether to add a progress bar to the gif. Defaults to True.
        progress_bar_color (str, optional): The color of the progress bar, can be color name or hex code. Defaults to 'blue'.
        progress_bar_height (int, optional): The height of the progress bar. Defaults to 5.
        add_text (bool, optional): Whether to add text to the gif. Defaults to False.
        text_xy (tuple, optional): The x, y coordinates of the text. For example, ('10%', '10%').
            Defaults to None, using the bottom left corner.
        text_sequence (list, optional): The sequence of text to add to the gif. For example, ['year 1', 'year 2', ...].
        font_type (str, optional): The font type of the text, can be 'arial.ttf' or 'alibaba.otf', or any system font. Defaults to 'arial.ttf'.
        font_size (int, optional): The font size of the text. Defaults to 20.
        font_color (str, optional): The color of the text, can be color name or hex code. Defaults to 'black'.
        mp4 (bool, optional): Whether to convert the gif to mp4. Defaults to False.
        quiet (bool, optional): Whether to print the progress. Defaults to False.
        reduce_size (bool, optional): Whether to reduce the size of the gif using ffmpeg. Defaults to False.
        clean_up (bool, optional): Whether to clean up the temporary files. Defaults to True.

    """

    import glob
    import tempfile

    if isinstance(images, str):
        if not images.endswith(ext):
            images = os.path.join(images, f"*{ext}")
        images = list(glob.glob(images))

    if not isinstance(images, list):
        raise ValueError("images must be a list or a path to the image directory.")

    images.sort()

    temp_dir = os.path.join(tempfile.gettempdir(), "timelapse")
    if not os.path.exists(temp_dir):
        os.makedirs(temp_dir)

    if bbox is not None:
        clip_dir = os.path.join(tempfile.gettempdir(), "clip")
        if not os.path.exists(clip_dir):
            os.makedirs(clip_dir)

        if len(bbox) == 4:
            bbox = bbox_to_geojson(bbox)

    else:
        clip_dir = None

    output = widgets.Output()

    if "out_ext" in kwargs:
        out_ext = kwargs["out_ext"].lower()
    else:
        out_ext = ".jpg"

    try:
        for index, image in enumerate(images):
            if bbox is not None:
                clip_file = os.path.join(clip_dir, os.path.basename(image))
                with output:
                    clip_image(image, mask=bbox, output=clip_file, to_cog=False)
                image = clip_file

            if "add_prefix" in kwargs:
                basename = (
                    str(f"{index + 1}").zfill(len(str(len(images))))
                    + "-"
                    + os.path.basename(image).replace(ext, out_ext)
                )
            else:
                basename = os.path.basename(image).replace(ext, out_ext)
            if not quiet:
                print(f"Processing {index+1}/{len(images)}: {basename} ...")

            # ignore GDAL warnings
            with output:
                numpy_to_image(
                    image, os.path.join(temp_dir, basename), bands=bands, size=size
                )
        make_gif(
            temp_dir,
            out_gif,
            ext=out_ext,
            fps=fps,
            loop=loop,
            mp4=mp4,
            clean_up=clean_up,
        )

        if clip_dir is not None:
            shutil.rmtree(clip_dir)

        if add_text:
            add_text_to_gif(
                out_gif,
                out_gif,
                text_xy,
                text_sequence,
                font_type,
                font_size,
                font_color,
                add_progress_bar,
                progress_bar_color,
                progress_bar_height,
                1000 / fps,
                loop,
            )
        elif add_progress_bar:
            add_progress_bar_to_gif(
                out_gif,
                out_gif,
                progress_bar_color,
                progress_bar_height,
                1000 / fps,
                loop,
            )

        if reduce_size:
            reduce_gif_size(out_gif)
    except Exception as e:
        print(e)


def gif_to_mp4(in_gif, out_mp4):
    """Converts a gif to mp4.

    Args:
        in_gif (str): The input gif file.
        out_mp4 (str): The output mp4 file.
    """
    from PIL import Image

    if not os.path.exists(in_gif):
        raise FileNotFoundError(f"{in_gif} does not exist.")

    out_mp4 = os.path.abspath(out_mp4)
    if not out_mp4.endswith(".mp4"):
        out_mp4 = out_mp4 + ".mp4"

    if not os.path.exists(os.path.dirname(out_mp4)):
        os.makedirs(os.path.dirname(out_mp4))

    if not is_tool("ffmpeg"):
        print("ffmpeg is not installed on your computer.")
        return

    width, height = Image.open(in_gif).size

    if width % 2 == 0 and height % 2 == 0:
        cmd = f"ffmpeg -loglevel error -i {in_gif} -vcodec libx264 -crf 25 -pix_fmt yuv420p {out_mp4}"
        os.system(cmd)
    else:
        width += width % 2
        height += height % 2
        cmd = f"ffmpeg -loglevel error -i {in_gif} -vf scale={width}:{height} -vcodec libx264 -crf 25 -pix_fmt yuv420p {out_mp4}"
        os.system(cmd)

    if not os.path.exists(out_mp4):
        raise Exception(f"Failed to create mp4 file.")


def merge_gifs(in_gifs, out_gif):
    """Merge multiple gifs into one.

    Args:
        in_gifs (str | list): The input gifs as a list or a directory path.
        out_gif (str): The output gif.

    Raises:
        Exception:  Raise exception when gifsicle is not installed.
    """
    import glob

    try:
        if isinstance(in_gifs, str) and os.path.isdir(in_gifs):
            in_gifs = glob.glob(os.path.join(in_gifs, "*.gif"))
        elif not isinstance(in_gifs, list):
            raise Exception("in_gifs must be a list.")

        in_gifs = " ".join(in_gifs)

        cmd = f"gifsicle {in_gifs} > {out_gif}"
        os.system(cmd)

    except Exception as e:
        print(
            "gifsicle is not installed. Run 'sudo apt-get install -y gifsicle' to install it."
        )
        print(e)


def gif_to_png(in_gif, out_dir=None, prefix="", verbose=True):
    """Converts a gif to png.

    Args:
        in_gif (str): The input gif file.
        out_dir (str, optional): The output directory. Defaults to None.
        prefix (str, optional): The prefix of the output png files. Defaults to None.
        verbose (bool, optional): Whether to print the progress. Defaults to True.

    Raises:
        FileNotFoundError: Raise exception when the input gif does not exist.
        Exception: Raise exception when ffmpeg is not installed.
    """
    import tempfile

    in_gif = os.path.abspath(in_gif)
    if " " in in_gif:
        raise Exception("in_gif cannot contain spaces.")
    if not os.path.exists(in_gif):
        raise FileNotFoundError(f"{in_gif} does not exist.")

    basename = os.path.basename(in_gif).replace(".gif", "")
    if out_dir is None:
        out_dir = os.path.join(tempfile.gettempdir(), basename)
        if not os.path.exists(out_dir):
            os.makedirs(out_dir)
    elif isinstance(out_dir, str) and not os.path.exists(out_dir):
        os.makedirs(out_dir)
    elif not isinstance(out_dir, str):
        raise Exception("out_dir must be a string.")

    out_dir = os.path.abspath(out_dir)
    cmd = f"ffmpeg -loglevel error -i {in_gif} -vsync 0 {out_dir}/{prefix}%d.png"
    os.system(cmd)

    if verbose:
        print(f"Images are saved to {out_dir}")


def gif_fading(in_gif, out_gif, duration=1, verbose=True):
    """Fade in/out the gif.

    Args:
        in_gif (str): The input gif file. Can be a directory path or http URL, e.g., "https://i.imgur.com/ZWSZC5z.gif"
        out_gif (str): The output gif file.
        duration (float, optional): The duration of the fading. Defaults to 1.
        verbose (bool, optional): Whether to print the progress. Defaults to True.

    Raises:
        FileNotFoundError: Raise exception when the input gif does not exist.
        Exception: Raise exception when ffmpeg is not installed.
    """
    import glob
    import tempfile

    current_dir = os.getcwd()

    if isinstance(in_gif, str) and in_gif.startswith("http"):
        ext = os.path.splitext(in_gif)[1]
        file_path = temp_file_path(ext)
        download_from_url(in_gif, file_path, verbose=verbose)
        in_gif = file_path

    in_gif = os.path.abspath(in_gif)
    if not in_gif.endswith(".gif"):
        raise Exception("in_gif must be a gif file.")

    if " " in in_gif:
        raise Exception("The filename cannot contain spaces.")

    out_gif = os.path.abspath(out_gif)
    if not os.path.exists(os.path.dirname(out_gif)):
        os.makedirs(os.path.dirname(out_gif))

    if not os.path.exists(in_gif):
        raise FileNotFoundError(f"{in_gif} does not exist.")

    basename = os.path.basename(in_gif).replace(".gif", "")
    temp_dir = os.path.join(tempfile.gettempdir(), basename)
    if os.path.exists(temp_dir):
        shutil.rmtree(temp_dir)

    gif_to_png(in_gif, temp_dir, verbose=verbose)

    os.chdir(temp_dir)

    images = list(glob.glob(os.path.join(temp_dir, "*.png")))
    count = len(images)

    files = []
    for i in range(1, count + 1):
        files.append(f"-loop 1 -t {duration} -i {i}.png")
    inputs = " ".join(files)

    filters = []
    for i in range(1, count):
        if i == 1:
            filters.append(
                f"\"[1:v][0:v]blend=all_expr='A*(if(gte(T,3),1,T/3))+B*(1-(if(gte(T,3),1,T/3)))'[v0];"
            )
        else:
            filters.append(
                f"[{i}:v][{i-1}:v]blend=all_expr='A*(if(gte(T,3),1,T/3))+B*(1-(if(gte(T,3),1,T/3)))'[v{i-1}];"
            )

    last_filter = ""
    for i in range(count - 1):
        last_filter += f"[v{i}]"
    last_filter += f'concat=n={count-1}:v=1:a=0[v]" -map "[v]"'
    filters.append(last_filter)
    filters = " ".join(filters)

    cmd = f"ffmpeg -y -loglevel error {inputs} -filter_complex {filters} {out_gif}"

    os.system(cmd)
    try:
        shutil.rmtree(temp_dir)
    except Exception as e:
        print(e)

    os.chdir(current_dir)


def is_tool(name):
    """Check whether `name` is on PATH and marked as executable."""

    return shutil.which(name) is not None


def vector_to_gif(
    filename,
    out_gif,
    colname,
    vmin=None,
    vmax=None,
    step=1,
    facecolor="black",
    figsize=(10, 8),
    padding=3,
    title=None,
    add_text=True,
    xy=("1%", "1%"),
    fontsize=20,
    add_progress_bar=True,
    progress_bar_color="blue",
    progress_bar_height=5,
    dpi=300,
    fps=10,
    loop=0,
    mp4=False,
    keep_png=False,
    verbose=True,
    open_args={},
    plot_args={},
):
    """Convert a vector to a gif. This function was inspired by by Johannes Uhl's shapefile2gif repo at
            https://github.com/johannesuhl/shapefile2gif. Credits to Johannes Uhl.

    Args:
        filename (str): The input vector file. Can be a directory path or http URL, e.g., "https://i.imgur.com/ZWSZC5z.gif"
        out_gif (str): The output gif file.
        colname (str): The column name of the vector that contains numerical values.
        vmin (float, optional): The minimum value to filter the data. Defaults to None.
        vmax (float, optional): The maximum value to filter the data. Defaults to None.
        step (float, optional): The step to filter the data. Defaults to 1.
        facecolor (str, optional): The color to visualize the data. Defaults to "black".
        figsize (tuple, optional): The figure size. Defaults to (10, 8).
        padding (int, optional): The padding of the figure tight_layout. Defaults to 3.
        title (str, optional): The title of the figure. Defaults to None.
        add_text (bool, optional): Whether to add text to the figure. Defaults to True.
        xy (tuple, optional): The position of the text from the lower-left corner. Defaults to ("1%", "1%").
        fontsize (int, optional): The font size of the text. Defaults to 20.
        add_progress_bar (bool, optional): Whether to add a progress bar to the figure. Defaults to True.
        progress_bar_color (str, optional): The color of the progress bar. Defaults to "blue".
        progress_bar_height (int, optional): The height of the progress bar. Defaults to 5.
        dpi (int, optional): The dpi of the figure. Defaults to 300.
        fps (int, optional): The frames per second (fps) of the gif. Defaults to 10.
        loop (int, optional): The number of loops of the gif. Defaults to 0, infinite loop.
        mp4 (bool, optional): Whether to convert the gif to mp4. Defaults to False.
        keep_png (bool, optional): Whether to keep the png files. Defaults to False.
        verbose (bool, optional): Whether to print the progress. Defaults to True.
        open_args (dict, optional): The arguments for the geopandas.read_file() function. Defaults to {}.
        plot_args (dict, optional): The arguments for the geopandas.GeoDataFrame.plot() function. Defaults to {}.

    """
    import geopandas as gpd
    import matplotlib.pyplot as plt

    out_dir = os.path.dirname(out_gif)
    tmp_dir = os.path.join(out_dir, "tmp_png")
    if not os.path.exists(tmp_dir):
        os.makedirs(tmp_dir)

    if isinstance(filename, str):
        gdf = gpd.read_file(filename, **open_args)
    elif isinstance(filename, gpd.GeoDataFrame):
        gdf = filename
    else:
        raise ValueError(
            "filename must be a string or a geopandas.GeoDataFrame object."
        )

    bbox = gdf.total_bounds

    if colname not in gdf.columns:
        raise Exception(
            f"{colname} is not in the columns of the GeoDataFrame. It must be one of {gdf.columns}"
        )

    values = gdf[colname].unique().tolist()
    values.sort()

    if vmin is None:
        vmin = values[0]
    if vmax is None:
        vmax = values[-1]

    options = range(vmin, vmax + step, step)

    W = bbox[2] - bbox[0]
    H = bbox[3] - bbox[1]

    if xy is None:
        # default text location is 5% width and 5% height of the image.
        xy = (int(0.05 * W), int(0.05 * H))
    elif (xy is not None) and (not isinstance(xy, tuple)) and (len(xy) == 2):
        raise Exception("xy must be a tuple, e.g., (10, 10), ('10%', '10%')")

    elif all(isinstance(item, int) for item in xy) and (len(xy) == 2):
        x, y = xy
        if (x > 0) and (x < W) and (y > 0) and (y < H):
            pass
        else:
            print(
                f"xy is out of bounds. x must be within [0, {W}], and y must be within [0, {H}]"
            )
            return
    elif all(isinstance(item, str) for item in xy) and (len(xy) == 2):
        x, y = xy
        if ("%" in x) and ("%" in y):
            try:
                x = float(x.replace("%", "")) / 100.0 * W
                y = float(y.replace("%", "")) / 100.0 * H
            except Exception:
                raise Exception(
                    "The specified xy is invalid. It must be formatted like this ('10%', '10%')"
                )
    else:
        raise Exception(
            "The specified xy is invalid. It must be formatted like this: (10, 10) or ('10%', '10%')"
        )

    x = bbox[0] + x
    y = bbox[1] + y

    for index, v in enumerate(options):
        if verbose:
            print(f"Processing {index+1}/{len(options)}: {v}...")
        yrdf = gdf[gdf[colname] <= v]
        fig, ax = plt.subplots()
        ax = yrdf.plot(facecolor=facecolor, figsize=figsize, **plot_args)
        ax.set_title(title, fontsize=fontsize)
        ax.set_axis_off()
        ax.set_xlim([bbox[0], bbox[2]])
        ax.set_ylim([bbox[1], bbox[3]])
        if add_text:
            ax.text(x, y, v, fontsize=fontsize)
        fig = ax.get_figure()
        plt.tight_layout(pad=padding)
        fig.savefig(tmp_dir + os.sep + "%s.png" % v, dpi=dpi)
        plt.clf()
        plt.close("all")

    png_to_gif(tmp_dir, out_gif, fps=fps, loop=loop)

    if add_progress_bar:
        add_progress_bar_to_gif(
            out_gif,
            out_gif,
            progress_bar_color,
            progress_bar_height,
            duration=1000 / fps,
            loop=loop,
        )

    if mp4:
        gif_to_mp4(out_gif, out_gif.replace(".gif", ".mp4"))

    if not keep_png:
        shutil.rmtree(tmp_dir)

    if verbose:
        print(f"Done. The GIF is saved to {out_gif}.")


def save_colorbar(
    out_fig=None,
    width=4.0,
    height=0.3,
    vmin=0,
    vmax=1.0,
    palette=None,
    vis_params=None,
    cmap="gray",
    discrete=False,
    label=None,
    label_size=10,
    label_weight="normal",
    tick_size=8,
    bg_color="white",
    orientation="horizontal",
    dpi="figure",
    transparent=False,
    show_colorbar=True,
    **kwargs: Any,
):
    """Create a standalone colorbar and save it as an image.

    Args:
        out_fig (str): Path to the output image.
        width (float): Width of the colorbar in inches. Default is 4.0.
        height (float): Height of the colorbar in inches. Default is 0.3.
        vmin (float): Minimum value of the colorbar. Default is 0.
        vmax (float): Maximum value of the colorbar. Default is 1.0.
        palette (list): List of colors to use for the colorbar. It can also be a cmap name, such as ndvi, ndwi, dem, coolwarm. Default is None.
        vis_params (dict): Visualization parameters as a dictionary. See https://developers.google.com/earth-engine/guides/image_visualization for options.
        cmap (str, optional): Matplotlib colormap. Defaults to "gray". See https://matplotlib.org/3.3.4/tutorials/colors/colormaps.html#sphx-glr-tutorials-colors-colormaps-py for options.
        discrete (bool, optional): Whether to create a discrete colorbar. Defaults to False.
        label (str, optional): Label for the colorbar. Defaults to None.
        label_size (int, optional): Font size for the colorbar label. Defaults to 12.
        label_weight (str, optional): Font weight for the colorbar label, can be "normal", "bold", etc. Defaults to "normal".
        tick_size (int, optional): Font size for the colorbar tick labels. Defaults to 10.
        bg_color (str, optional): Background color for the colorbar. Defaults to "white".
        orientation (str, optional): Orientation of the colorbar, such as "vertical" and "horizontal". Defaults to "horizontal".
        dpi (float | str, optional): The resolution in dots per inch.  If 'figure', use the figure's dpi value. Defaults to "figure".
        transparent (bool, optional): Whether to make the background transparent. Defaults to False.
        show_colorbar (bool, optional): Whether to show the colorbar. Defaults to True.
        **kwargs: Other keyword arguments to pass to matplotlib.pyplot.savefig().

    Returns:
        Path to the output image.
    """
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    import numpy as np
    import matplotlib.ticker as mticker  # <-- add

    from .colormaps import get_palette, palettes

    ticks = kwargs.pop("ticks", None)  # list of tick positions
    tick_step = kwargs.pop("tick_step", None)  # e.g., 10
    integer_ticks = kwargs.pop("integer_ticks", False)

    if out_fig is None:
        out_fig = temp_file_path("png")
    else:
        out_fig = check_file_path(out_fig)

    if vis_params is None:
        vis_params = {}
    elif not isinstance(vis_params, dict):
        raise TypeError("The vis_params must be a dictionary.")

    if palette is not None:
        if palette in ["ndvi", "ndwi", "dem"]:
            palette = palettes[palette]
        elif palette in list(palettes.keys()):
            palette = get_palette(palette)
        vis_params["palette"] = palette

    orientation = orientation.lower()
    if orientation not in ["horizontal", "vertical"]:
        raise ValueError("The orientation must be either horizontal or vertical.")

    if "opacity" in vis_params:
        alpha = vis_params["opacity"]
        if type(alpha) not in (int, float):
            raise ValueError("The provided opacity value must be type scalar.")
    else:
        alpha = 1

    if "palette" in vis_params:
        hexcodes = to_hex_colors(vis_params["palette"])
        if discrete:
            cmap = mpl.colors.ListedColormap(hexcodes)
            vals = np.linspace(vmin, vmax, cmap.N + 1)
            norm = mpl.colors.BoundaryNorm(vals, cmap.N)

        else:
            cmap = mpl.colors.LinearSegmentedColormap.from_list(
                "custom", hexcodes, N=256
            )
            norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)

    elif cmap is not None:
        cmap = mpl.colormaps[cmap]
        norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)

    else:
        raise ValueError(
            'cmap keyword or "palette" key in vis_params must be provided.'
        )

    fig, ax = plt.subplots(figsize=(width, height))
    cb = mpl.colorbar.ColorbarBase(
        ax, norm=norm, alpha=alpha, cmap=cmap, orientation=orientation, **kwargs
    )

    # remove minor ticks entirely
    cb.ax.minorticks_off()

    # --- NEW: force integer ticks/labels ---
    axis = cb.ax.xaxis if orientation == "horizontal" else cb.ax.yaxis
    if ticks is not None:
        cb.set_ticks(ticks)
    elif tick_step is not None:
        cb.set_ticks(np.arange(vmin, vmax + 0.5 * float(tick_step), float(tick_step)))
    elif integer_ticks:
        axis.set_major_locator(mticker.MaxNLocator(integer=True))

    # always show integers (no decimals) if any of the above applied
    if ticks is not None or tick_step is not None or integer_ticks:
        axis.set_major_formatter(mticker.StrMethodFormatter("{x:.0f}"))
    # --------------------------------------

    if label is not None:
        cb.set_label(label=label, size=label_size, weight=label_weight)
    cb.ax.tick_params(labelsize=tick_size)

    if transparent:
        bg_color = None

    if bg_color is not None:
        kwargs["facecolor"] = bg_color
    if "bbox_inches" not in kwargs:
        kwargs["bbox_inches"] = "tight"

    fig.savefig(out_fig, dpi=dpi, transparent=transparent, **kwargs)
    if not show_colorbar:
        plt.close(fig)
    return out_fig


def is_arcpy():
    """Check if arcpy is available.

    Returns:
        True if arcpy is available, False otherwise.
    """
    import sys

    if "arcpy" in sys.modules:
        return True
    else:
        return False


def arc_active_map() -> Optional[Any]:
    """Get the active map in ArcGIS Pro.

    Returns:
        The active map in ArcGIS Pro.
    """
    if is_arcpy():
        import arcpy  # pylint: disable=E0401

        aprx = arcpy.mp.ArcGISProject("CURRENT")
        m = aprx.activeMap
        return m
    else:
        return None


def arc_active_view() -> Optional[Any]:
    """Get the active view in ArcGIS Pro.

    Returns:
        The active view in ArcGIS Pro.
    """
    if is_arcpy():
        import arcpy  # pylint: disable=E0401

        aprx = arcpy.mp.ArcGISProject("CURRENT")
        view = aprx.activeView
        return view
    else:
        return None


def arc_add_layer(url, name=None, shown=True, opacity=1.0):
    """Add a layer to the active map in ArcGIS Pro.

    Args:
        url (str): The URL of the tile layer to add.
        name (str, optional): The name of the layer. Defaults to None.
        shown (bool, optional): Whether the layer is shown. Defaults to True.
        opacity (float, optional): The opacity of the layer. Defaults to 1.0.
    """
    if is_arcpy():
        m = arc_active_map()
        if m is not None:
            m.addDataFromPath(url)
            if isinstance(name, str):
                layers = m.listLayers("Tiled service layer")
                if len(layers) > 0:
                    layer = layers[0]
                    layer.name = name
                    layer.visible = shown
                    layer.transparency = 100 - (opacity * 100)


def arc_zoom_to_extent(xmin, ymin, xmax, ymax):
    """Zoom to an extent in ArcGIS Pro.

    Args:
        xmin (float): The minimum x value of the extent.
        ymin (float): The minimum y value of the extent.
        xmax (float): The maximum x value of the extent.
        ymax (float): The maximum y value of the extent.
    """
    if is_arcpy():
        import arcpy  # pylint: disable=E0401

        view = arc_active_view()
        if view is not None:
            view.camera.setExtent(
                arcpy.Extent(
                    xmin,
                    ymin,
                    xmax,
                    ymax,
                    spatial_reference=arcpy.SpatialReference(4326),
                )
            )

        # if isinstance(zoom, int):
        #     scale = 156543.04 * math.cos(0) / math.pow(2, zoom)
        #     view.camera.scale = scale  # Not working properly


def arc_zoom_to_bounds(bounds):
    """Zoom to a bounding box.

    Args:
        bounds (list): The bounding box to zoom to in the form [xmin, ymin, xmax, ymax] or [(ymin, xmin), (ymax, xmax)].

    Raises:
        ValueError: _description_
    """

    if len(bounds) == 4:
        xmin, ymin, xmax, ymax = bounds
    elif len(bounds) == 2:
        (ymin, xmin), (ymax, xmax) = bounds
    else:
        raise ValueError("bounds must be a tuple of length 2 or 4.")

    arc_zoom_to_extent(xmin, ymin, xmax, ymax)


def vector_to_raster(
    vector,
    output,
    field="FID",
    assign="last",
    nodata=True,
    cell_size=None,
    base=None,
    callback=None,
    verbose=False,
    to_epsg=None,
):
    """Convert a vector to a raster.

    Args:
        vector (str | GeoPandas.GeoDataFrame): The input vector data, can be a file path or a GeoDataFrame.
        output (str): The output raster file path.
        field (str, optional): Input field name in attribute table. Defaults to 'FID'.
        assign (str, optional): Assignment operation, where multiple points are in the same grid cell; options
            include 'first', 'last' (default), 'min', 'max', 'sum', 'number'. Defaults to 'last'.
        nodata (bool, optional): Background value to set to NoData. Without this flag, it will be set to 0.0.
        cell_size (float, optional): Optionally specified cell size of output raster. Not used when base raster is specified
        base (str, optional): Optionally specified input base raster file. Not used when a cell size is specified. Defaults to None.
        callback (fuct, optional): A callback function to report progress. Defaults to None.
        verbose (bool, optional): Whether to print progress to the console. Defaults to False.
        to_epsg (integer, optional): Optionally specified the EPSG code to reproject the raster to. Defaults to None.

    """
    import geopandas as gpd
    import whitebox

    output = os.path.abspath(output)

    if isinstance(vector, str):
        gdf = gpd.read_file(vector)
    elif isinstance(vector, gpd.GeoDataFrame):
        gdf = vector
    else:
        raise TypeError("vector must be a file path or a GeoDataFrame")

    if to_epsg is None:
        to_epsg = 3857

    if to_epsg == 4326:
        raise ValueError("to_epsg cannot be 4326")

    if gdf.crs.is_geographic:
        gdf = gdf.to_crs(epsg=to_epsg)
        vector = temp_file_path(extension=".shp")
        gdf.to_file(vector)
    else:
        to_epsg = gdf.crs.to_epsg()

    wbt = whitebox.WhiteboxTools()
    wbt.verbose = verbose

    goem_type = gdf.geom_type[0]

    if goem_type == "LineString":
        wbt.vector_lines_to_raster(
            vector, output, field, nodata, cell_size, base, callback
        )
    elif goem_type == "Polygon":
        wbt.vector_polygons_to_raster(
            vector, output, field, nodata, cell_size, base, callback
        )
    else:
        wbt.vector_points_to_raster(
            vector, output, field, assign, nodata, cell_size, base, callback
        )

    image_set_crs(output, to_epsg)


def show_youtube_video(url, width=800, height=450, allow_autoplay=False, **kwargs: Any):
    """
    Displays a Youtube video in a Jupyter notebook.

    Args:
        url (string): a link to a Youtube video.
        width (int, optional): the width of the video. Defaults to 800.
        height (int, optional): the height of the video. Defaults to 600.
        allow_autoplay (bool, optional): whether to allow autoplay. Defaults to False.
        **kwargs (Any): further arguments for IPython.display.YouTubeVideo

    Returns:
        A video that is displayed in your notebook.
    """
    import re

    from IPython.display import YouTubeVideo

    if not isinstance(url, str):
        raise TypeError("URL must be a string")

    match = re.match(
        r"^https?:\/\/(?:www\.)?youtube\.com\/watch\?(?=.*v=([^\s&]+)).*$|^https?:\/\/(?:www\.)?youtu\.be\/([^\s&]+).*$",
        url,
    )
    if not match:
        raise ValueError("Invalid YouTube video URL")

    video_id = match.group(1) if match.group(1) else match.group(2)

    return YouTubeVideo(
        video_id, width=width, height=height, allow_autoplay=allow_autoplay, **kwargs
    )


def html_to_gradio(html, width="100%", height="500px", **kwargs: Any):
    """Converts the map to an HTML string that can be used in Gradio. Removes unsupported elements, such as
        attribution and any code blocks containing functions. See https://github.com/gradio-app/gradio/issues/3190

    Args:
        width (str, optional): The width of the map. Defaults to '100%'.
        height (str, optional): The height of the map. Defaults to '500px'.

    Returns:
        The HTML string to use in Gradio.
    """

    if isinstance(width, int):
        width = f"{width}px"

    if isinstance(height, int):
        height = f"{height}px"

    if isinstance(html, str):
        with open(html, "r") as f:
            lines = f.readlines()
    elif isinstance(html, list):
        lines = html
    else:
        raise TypeError("html must be a file path or a list of strings")

    output = []
    skipped_lines = []
    for index, line in enumerate(lines):
        if index in skipped_lines:
            continue
        if line.lstrip().startswith('{"attribution":'):
            continue
        elif "on(L.Draw.Event.CREATED, function(e)" in line:
            for i in range(14):
                skipped_lines.append(index + i)
        elif "L.Control.geocoder" in line:
            for i in range(5):
                skipped_lines.append(index + i)
        elif "function(e)" in line:
            print(
                f"Warning: The folium plotting backend does not support functions in code blocks. Please delete line {index + 1}."
            )
        else:
            output.append(line + "\n")

    return f"""<iframe style="width: {width}; height: {height}" name="result" allow="midi; geolocation; microphone; camera;
    display-capture; encrypted-media;" sandbox="allow-modals allow-forms
    allow-scripts allow-same-origin allow-popups
    allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
    allowpaymentrequest="" frameborder="0" srcdoc='{"".join(output)}'></iframe>"""


def filter_bounds(data, bbox, within=False, align=True, **kwargs: Any) -> Any:
    """Filters a GeoDataFrame or GeoSeries by a bounding box.

    Args:
        data (str | GeoDataFrame): The input data to filter. Can be a file path or a GeoDataFrame.
        bbox (list | GeoDataFrame): The bounding box to filter by. Can be a list of 4 coordinates or a file path or a GeoDataFrame.
        within (bool, optional): Whether to filter by the bounding box or the bounding box's interior. Defaults to False.
        align (bool, optional): If True, automatically aligns GeoSeries based on their indices. If False, the order of elements is preserved.

    Returns:
        The filtered data.
    """
    import geopandas as gpd

    if isinstance(data, str):
        data = gpd.read_file(data, **kwargs)
    elif not isinstance(data, (gpd.GeoDataFrame, gpd.GeoSeries)):
        raise TypeError("data must be a file path or a GeoDataFrame or GeoSeries")

    if isinstance(bbox, list):
        if len(bbox) != 4:
            raise ValueError("bbox must be a list of 4 coordinates")
        bbox = bbox_to_gdf(bbox)
    elif isinstance(bbox, str):
        bbox = gpd.read_file(bbox, **kwargs)

    if within:
        result = data[data.within(bbox.union_all(), align=align)]
    else:
        result = data[data.intersects(bbox.union_all(), align=align)]

    return result


def filter_date(
    data, start_date=None, end_date=None, date_field="date", date_args={}, **kwargs
) -> Any:
    """Filters a DataFrame, GeoDataFrame or GeoSeries by a date range.

    Args:
        data (str | DataFrame | GeoDataFrame): The input data to filter. Can be a file path or a DataFrame or GeoDataFrame.
        start_date (str, optional): The start date, e.g., 2023-01-01. Defaults to None.
        end_date (str, optional): The end date, e.g., 2023-12-31. Defaults to None.
        date_field (str, optional): The name of the date field. Defaults to "date".
        date_args (dict, optional): Additional arguments for pd.to_datetime. Defaults to {}.

    Returns:
        The filtered data.
    """

    import datetime

    import geopandas as gpd
    import pandas as pd

    if isinstance(data, str):
        data = gpd.read_file(data, **kwargs)
    elif not isinstance(
        data, (gpd.GeoDataFrame, gpd.GeoSeries, pd.DataFrame, pd.Series)
    ):
        raise TypeError("data must be a file path or a GeoDataFrame or GeoSeries")

    if date_field not in data.columns:
        raise ValueError(f"date_field must be one of {data.columns}")

    new_field = f"{date_field}_temp"
    data[new_field] = pd.to_datetime(data[date_field], **date_args)

    if end_date is None:
        end_date = datetime.datetime.now().strftime("%Y-%m-%d")

    if start_date is None:
        start_date = data[new_field].min()

    mask = (data[new_field] >= start_date) & (data[new_field] <= end_date)
    result = data.loc[mask]
    return result.drop(columns=[new_field], axis=1)


def skip_mkdocs_build():
    """Skips the MkDocs build if the USE_MKDOCS environment variable is set.

    Returns:
        Whether to skip the MkDocs build.
    """
    if os.environ.get("USE_MKDOCS") is not None:
        return True
    else:
        return False


def disjoint(input_features, selecting_features, output=None, **kwargs: Any) -> Any:
    """Find the features in the input_features that do not intersect the selecting_features.

    Args:
        input_features (str | GeoDataFrame): The input features to select from. Can be a file path or a GeoDataFrame.
        selecting_features (str | GeoDataFrame): The features in the Input Features parameter will be selected based
            on their relationship to the features from this layer.
        output (are, optional): The output path to save the GeoDataFrame in a vector format (e.g., shapefile). Defaults to None.

    Returns:
        The path to the output file or the GeoDataFrame.
    """
    import geopandas as gpd

    if isinstance(input_features, str):
        input_features = gpd.read_file(input_features, **kwargs)
    elif not isinstance(input_features, gpd.GeoDataFrame):
        raise TypeError("input_features must be a file path or a GeoDataFrame")

    if isinstance(selecting_features, str):
        selecting_features = gpd.read_file(selecting_features, **kwargs)
    elif not isinstance(selecting_features, gpd.GeoDataFrame):
        raise TypeError("selecting_features must be a file path or a GeoDataFrame")

    selecting_features = selecting_features.to_crs(input_features.crs)

    input_features["savedindex"] = input_features.index
    intersecting = selecting_features.sjoin(input_features, how="inner")["savedindex"]
    results = input_features[~input_features.savedindex.isin(intersecting)].drop(
        columns=["savedindex"], axis=1
    )

    if output is not None:
        results.to_file(output, **kwargs)
    else:
        return results


def zonal_stats(
    vectors,
    raster,
    layer=0,
    band_num=1,
    nodata=None,
    affine=None,
    stats=None,
    all_touched=False,
    categorical=False,
    category_map=None,
    add_stats=None,
    raster_out=False,
    prefix=None,
    geojson_out=False,
    gdf_out=False,
    dst_crs=None,
    open_vector_args={},
    open_raster_args={},
    **kwargs: Any,
):
    """This function wraps rasterstats.zonal_stats and performs reprojection if necessary.
        See https://pythonhosted.org/rasterstats/rasterstats.html.

    Args:
        vectors (str | list | GeoDataFrame): path to an vector source or geo-like python objects.
        raster (str | ndarray): ndarray or path to a GDAL raster source.
        layer (int, optional): If vectors is a path to an fiona source, specify the vector layer to
            use either by name or number. Defaults to 0
        band_num (int | str, optional): If raster is a GDAL source, the band number to use (counting from 1). defaults to 1.
        nodata (float, optional): If raster is a GDAL source, this value overrides any NODATA value
            specified in the file's metadata. If None, the file's metadata's NODATA value (if any)
            will be used. defaults to None.
        affine (Affine, optional): required only for ndarrays, otherwise it is read from src. Defaults to None.
        stats (str | list, optional): Which statistics to calculate for each zone.
            It can be ['min', 'max', 'mean', 'count']. For more, see https://pythonhosted.org/rasterstats/manual.html#zonal-statistics
            Defaults to None.
        all_touched (bool, optional): Whether to include every raster cell touched by a geometry, or only those having
            a center point within the polygon. defaults to False
        categorical (bool, optional): If True, the raster values will be treated as categorical.
        category_map (dict, optional):A dictionary mapping raster values to human-readable categorical names.
            Only applies when categorical is True
        add_stats (dict, optional): with names and functions of additional stats to compute. Defaults to None.
        raster_out (bool, optional): Include the masked numpy array for each feature?. Defaults to False.
        prefix (str, optional): add a prefix to the keys. Defaults to None.
        geojson_out (bool, optional): Return list of GeoJSON-like features (default: False)
            Original feature geometry and properties will be retained with zonal stats
            appended as additional properties. Use with prefix to ensure unique and
            meaningful property names.. Defaults to False.
        gdf_out (bool, optional): Return a GeoDataFrame. Defaults to False.
        dst_crs (str, optional): The destination CRS. Defaults to None.
        open_vector_args (dict, optional): Pass additional arguments to geopandas.open_file(). Defaults to {}.
        open_raster_args (dict, optional): Pass additional arguments to rasterio.open(). Defaults to {}.

    Returns:
        The zonal statistics results.
    """

    import geopandas as gpd
    import rasterio
    import xarray as xr

    try:
        import rasterstats
    except ImportError:
        raise ImportError(
            "rasterstats is not installed. Install it with pip install rasterstats"
        )
    try:
        if isinstance(raster, str):
            with rasterio.open(raster, **open_raster_args) as src:
                affine = src.transform
                nodata = src.nodata
                array = src.read(band_num, masked=True)
                raster_crs = src.crs
        elif isinstance(raster, rasterio.io.DatasetReader):
            affine = raster.transform
            nodata = raster.nodata
            array = raster.read(band_num, masked=True)
            raster_crs = raster.crs
        elif isinstance(raster, xr.DataArray):
            array = raster
            raster_crs = raster.rio.crs
        else:
            array = raster

        if isinstance(vectors, str):
            gdf = gpd.read_file(vectors, **open_vector_args)
        elif isinstance(vectors, list):
            gdf = gpd.GeoDataFrame.from_features(vectors)
        else:
            gdf = vectors

        vector_crs = gdf.crs

        if gdf.crs.is_geographic:
            if not raster_crs.is_geographic:
                gdf = gdf.to_crs(raster_crs)
        elif gdf.crs != raster_crs:
            if not raster_crs.is_geographic:
                gdf = gdf.to_crs(raster_crs)
            else:
                raise ValueError("The vector and raster CRSs are not compatible")

        if gdf_out is True:
            geojson_out = True

        result = rasterstats.zonal_stats(
            gdf,
            array,
            layer=layer,
            band_num=band_num,
            nodata=nodata,
            affine=affine,
            stats=stats,
            all_touched=all_touched,
            categorical=categorical,
            category_map=category_map,
            add_stats=add_stats,
            raster_out=raster_out,
            prefix=prefix,
            geojson_out=geojson_out,
            **kwargs,
        )

        if gdf_out is True:
            if dst_crs is None:
                dst_crs = vector_crs

            out_gdf = gpd.GeoDataFrame.from_features(result)
            out_gdf.crs = raster_crs
            return out_gdf.to_crs(dst_crs)
        else:
            return result

    except Exception as e:
        raise Exception(e)


def s3_list_objects(
    bucket,
    prefix=None,
    limit=None,
    ext=None,
    fullpath=True,
    request_payer="bucket-owner",
    client_args={},
    **kwargs: Any,
):
    """List objects in a S3 bucket

    Args:
        bucket (str): The name of the bucket.
        prefix (str, optional): Limits the response to keys that begin with the specified prefix. Defaults to None.
        limit (init, optional): The maximum number of keys returned in the response body.
        ext (str, optional): Filter by file extension. Defaults to None.
        fullpath (bool, optional): Return full path. Defaults to True.
        request_payer (str, optional): Specifies who pays for the download from S3.
            Can be "bucket-owner" or "requester". Defaults to "bucket-owner".
        client_args (dict, optional): Additional arguments to pass to boto3.client(). Defaults to {}.

    Returns:
        List of objects.
    """
    try:
        import boto3
    except ImportError:
        raise ImportError("boto3 is not installed. Install it with pip install boto3")

    client = boto3.client("s3", **client_args)

    if prefix is not None:
        kwargs["Prefix"] = prefix

    files = []
    kwargs["RequestPayer"] = request_payer
    if isinstance(limit, int) and limit < 1000:
        kwargs["MaxKeys"] = limit
        response = client.list_objects_v2(Bucket=bucket, **kwargs)
        for obj in response["Contents"]:
            files.append(obj)
    else:
        paginator = client.get_paginator("list_objects_v2")
        pages = paginator.paginate(Bucket=bucket, **kwargs)

        for page in pages:
            files.extend(page.get("Contents", []))

    if ext is not None:
        files = [f for f in files if f["Key"].endswith(ext)]

    if fullpath:
        return [f"s3://{bucket}/{r['Key']}" for r in files]
    else:
        return [r["Key"] for r in files]


def s3_download_file(filename=None, bucket=None, key=None, outfile=None, **kwargs: Any):
    """Download a file from S3.

    Args:
        filename (str, optional): The full path to the file. Defaults to None.
        bucket (str, optional): The name of the bucket. Defaults to None.
        key (str, optional): The key of the file. Defaults to None.
        outfile (str, optional): The name of the output file. Defaults to None.
    Raises:
        ImportError: If boto3 is not installed.
    """

    if os.environ.get("USE_MKDOCS") is not None:
        return

    try:
        import boto3
    except ImportError:
        raise ImportError("boto3 is not installed. Install it with pip install boto3")

    client = boto3.client("s3", **kwargs)

    if filename is not None:
        bucket = filename.split("/")[2]
        key = "/".join(filename.split("/")[3:])

    if outfile is None:
        outfile = key.split("/")[-1]

    if not os.path.exists(outfile):
        client.download_file(bucket, key, outfile)
    else:
        print(f"File already exists: {outfile}")


def s3_download_files(
    filenames=None, bucket=None, keys=None, outdir=None, quiet=False, **kwargs
):
    """Download multiple files from S3.

    Args:
        filenames (list, optional): A list of filenames. Defaults to None.
        bucket (str, optional): The name of the bucket. Defaults to None.
        keys (list, optional): A list of keys. Defaults to None.
        outdir (str, optional): The name of the output directory. Defaults to None.
        quiet (bool, optional): Suppress output. Defaults to False.

    Raises:
        ValueError: If neither filenames or keys are provided.
    """

    if keys is None:
        keys = []

    if filenames is not None:
        if isinstance(filenames, list):
            for filename in filenames:
                bucket = filename.split("/")[2]
                key = "/".join(filename.split("/")[3:])
                keys.append(key)
    elif filenames is None and keys is None:
        raise ValueError("Either filenames or keys must be provided")

    for index, key in enumerate(keys):
        if outdir is not None:
            if not os.path.exists(outdir):
                os.makedirs(outdir)
            outfile = os.path.join(outdir, key.split("/")[-1])
        else:
            outfile = key.split("/")[-1]

        if not quiet:
            print(f"Downloading {index+1} of {len(keys)}: {outfile}")
        s3_download_file(bucket=bucket, key=key, outfile=outfile, **kwargs)


def s3_get_object(
    bucket,
    key,
    output=None,
    chunk_size=1024 * 1024,
    request_payer="bucket-owner",
    quiet=False,
    client_args={},
    **kwargs: Any,
):
    """Download a file from S3.

    Args:
        bucket (str): The name of the bucket.
        key (key): The key of the file.
        output (str, optional): The name of the output file. Defaults to None.
        chunk_size (int, optional): The chunk size in bytes. Defaults to 1024 * 1024.
        request_payer (str, optional): Specifies who pays for the download from S3.
        quiet (bool, optional): Suppress output. Defaults to False.
            Can be "bucket-owner" or "requester". Defaults to "bucket-owner".
        client_args (dict, optional): Additional arguments to pass to boto3.client(). Defaults to {}.
        **kwargs: Additional arguments to pass to boto3.client().get_object().
    """

    try:
        import boto3
    except ImportError:
        raise ImportError("boto3 is not installed. Install it with pip install boto3")

    # Set up the S3 client
    s3 = boto3.client("s3", **client_args)

    if output is None:
        output = key.split("/")[-1]

    out_dir = os.path.dirname(os.path.abspath(output))
    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    # Set up the progress bar
    def progress_callback(bytes_amount):
        # This function will be called by the StreamingBody object
        # to report the number of bytes downloaded so far
        total_size = int(response["ContentLength"])
        progress_percent = int(bytes_amount / total_size * 100)
        if not quiet:
            print(f"\rDownloading: {progress_percent}% complete.", end="")

    # Download the file
    response = s3.get_object(
        Bucket=bucket, Key=key, RequestPayer=request_payer, **kwargs
    )

    # Save the file to disk
    with open(output, "wb") as f:
        # Use the StreamingBody object to read the file in chunks
        # and track the download progress
        body = response["Body"]
        downloaded_bytes = 0
        for chunk in body.iter_chunks(chunk_size=chunk_size):
            f.write(chunk)
            downloaded_bytes += len(chunk)
            progress_callback(downloaded_bytes)


def s3_get_objects(
    bucket,
    keys=None,
    out_dir=None,
    prefix=None,
    limit=None,
    ext=None,
    chunk_size=1024 * 1024,
    request_payer="bucket-owner",
    quiet=True,
    client_args={},
    **kwargs: Any,
):
    """Download multiple files from S3.

    Args:
        bucket (str): The name of the bucket.
        keys (list, optional): A list of keys. Defaults to None.
        out_dir (str, optional): The name of the output directory. Defaults to None.
        prefix (str, optional): Limits the response to keys that begin with the specified prefix. Defaults to None.
        limit (int, optional): The maximum number of keys returned in the response body.
        ext (str, optional): Filter by file extension. Defaults to None.
        chunk_size (int, optional): The chunk size in bytes. Defaults to 1024 * 1024.
        request_payer (str, optional): Specifies who pays for the download from S3.
            Can be "bucket-owner" or "requester". Defaults to "bucket-owner".
        quiet (bool, optional): Suppress output. Defaults to True.
        client_args (dict, optional): Additional arguments to pass to boto3.client(). Defaults to {}.
        **kwargs: Additional arguments to pass to boto3.client().get_object().

    """

    try:
        import boto3
    except ImportError:
        raise ImportError("boto3 is not installed. Install it with pip install boto3")

    if out_dir is None:
        out_dir = os.getcwd()

    if keys is None:
        fullpath = False
        keys = s3_list_objects(
            bucket, prefix, limit, ext, fullpath, request_payer, client_args, **kwargs
        )

    for index, key in enumerate(keys):
        print(f"Downloading {index+1} of {len(keys)}: {key}")
        output = os.path.join(out_dir, key.split("/")[-1])
        s3_get_object(
            bucket, key, output, chunk_size, request_payer, quiet, client_args, **kwargs
        )


def s3_credential_provider(**kwargs: Any) -> "Boto3CredentialProvider":
    """Create a Boto3 credential provider for S3 authentication.

    This function creates a Boto3 credential provider that can be used with obstore
    for S3 authentication. It uses the default AWS credential chain (environment
    variables, AWS config files, IAM roles, etc.).

    Args:
        **kwargs: Additional keyword arguments to pass to the Boto3CredentialProvider.
                 Common options include:
                 - profile_name (str): AWS profile name to use
                 - region_name (str): AWS region name

    Returns:
        Boto3CredentialProvider: A credential provider instance that can be used
                                with obstore S3Store for authentication.

    Example:
        >>> provider = s3_credential_provider(profile_name='my-profile')
        >>> store = S3Store('my-bucket', credential_provider=provider)
    """
    from boto3 import Session
    from obstore.auth.boto3 import Boto3CredentialProvider

    session = Session()
    credential_provider = Boto3CredentialProvider(session, **kwargs)
    return credential_provider


def s3_list_directories(
    bucket: Optional[str] = None,
    prefix: Optional[str] = None,
    path: Optional[str] = None,
    region: Optional[str] = None,
    config: Optional[dict] = None,
    client_options: Optional[dict] = None,
    retry_config: Optional[dict] = None,
    credential_provider: Optional["Boto3CredentialProvider"] = None,
    skip_signature: bool = True,
    return_full_path: bool = False,
) -> List[str]:
    """List directories (common prefixes) in an S3 bucket.

    This function lists directories (common prefixes) in an S3 bucket using the
    obstore library. It can work with either bucket/prefix parameters or a full
    S3 path.

    Args:
        bucket (str, optional): The S3 bucket name. Required if path is not provided.
        prefix (str, optional): The prefix to filter directories. Required if path is not provided.
        path (str, optional): Full S3 path in format 's3://bucket/prefix'.
                             If provided, bucket and prefix are extracted from this.
        region (str, optional): AWS region where the bucket is located.
                               If not provided, uses default region.
        config (dict, optional): Additional S3 configuration options.
        client_options (dict, optional): Additional S3 client options.
        retry_config (dict, optional): Retry configuration for S3 operations.
        credential_provider (Boto3CredentialProvider, optional): Custom credential provider.
                                                                If not provided, creates a default one.
        skip_signature (bool, optional): Whether to skip request signing for public buckets.
                                        Defaults to True.
        return_full_path (bool, optional): Whether to return full S3 paths or just directory names.
                                          Defaults to False.

    Returns:
        List of directory names or full S3 paths, depending on return_full_path parameter.

    Raises:
        ValueError: If neither path nor both bucket and prefix are provided.

    Example:
        >>> # Using bucket and prefix
        >>> dirs = s3_list_directories(
        ...     bucket='my-bucket',
        ...     prefix='data/',
        ...     region='us-west-2'
        ... )

        >>> # Using full S3 path
        >>> dirs = s3_list_directories(
        ...     path='s3://my-bucket/data/',
        ...     region='us-west-2',
        ...     return_full_path=True
        ... )
    """
    import obstore as obs
    from obstore.store import S3Store

    if path is not None:
        bare_path = path.replace(f"s3://", "")
        bucket = bare_path.split("/")[0]
        prefix = bare_path.replace(bucket, "")[1:]
    elif bucket is not None and prefix is not None:
        pass
    else:
        raise ValueError("Either path or bucket and prefix must be provided")

    if credential_provider is None:
        credential_provider = s3_credential_provider()

    store = S3Store(
        bucket,
        region=region,
        config=config,
        client_options=client_options,
        retry_config=retry_config,
        credential_provider=credential_provider,
        skip_signature=skip_signature,
    )
    result = obs.list_with_delimiter(store, prefix=prefix)
    common_prefixes = result.get("common_prefixes", [])

    paths = []
    # Extract directory names from common prefixes
    for common_prefix in common_prefixes:
        if return_full_path:
            paths.append(f"s3://{bucket}/{common_prefix}")
        else:
            # Extract just the directory name (last part before trailing slash)
            dir_parts = common_prefix.rstrip("/").split("/")
            if dir_parts:
                paths.append(dir_parts[-1])
    return paths


def s3_list_files(
    bucket: Optional[str] = None,
    prefix: Optional[str] = None,
    path: Optional[str] = None,
    file_ext: Optional[str] = None,
    region: Optional[str] = None,
    config: Optional[dict] = None,
    client_options: Optional[dict] = None,
    retry_config: Optional[dict] = None,
    credential_provider: Optional["Boto3CredentialProvider"] = None,
    skip_signature: bool = True,
    return_full_path: bool = False,
) -> List[str]:
    """List files in an S3 bucket with optional filtering.

    This function lists files in an S3 bucket using the obstore library. It supports
    filtering by file extension and can return either relative paths or full S3 URLs.

    Args:
        bucket (str, optional): The S3 bucket name. Required if path is not provided.
        prefix (str, optional): The prefix to filter files. Required if path is not provided.
        path (str, optional): Full S3 path in format 's3://bucket/prefix'.
                             If provided, bucket and prefix are extracted from this.
        file_ext (str, optional): File extension to filter by (e.g., '.parquet', '.json').
                                 If provided, only files with this extension are returned.
        region (str, optional): AWS region where the bucket is located.
                               If not provided, uses default region.
        config (dict, optional): Additional S3 configuration options.
        client_options (dict, optional): Additional S3 client options.
        retry_config (dict, optional): Retry configuration for S3 operations.
        credential_provider (Boto3CredentialProvider, optional): Custom credential provider.
                                                                If not provided, creates a default one.
        skip_signature (bool, optional): Whether to skip request signing for public buckets.
                                        Defaults to True.
        return_full_path (bool, optional): Whether to return full S3 URLs or relative paths.
                                          Defaults to False.

    Returns:
        List of file paths. Format depends on return_full_path parameter:
                  - If True: Full S3 URLs like 's3://bucket/path/file.ext'
                  - If False: Relative paths like 'path/file.ext'

    Raises:
        ValueError: If neither path nor both bucket and prefix are provided.

    Example:
        >>> # List all files in a directory
        >>> files = s3_list_files(
        ...     bucket='my-bucket',
        ...     prefix='data/2024/',
        ...     region='us-west-2'
        ... )

        >>> # List only parquet files with full paths
        >>> parquet_files = s3_list_files(
        ...     path='s3://my-bucket/data/2024/',
        ...     file_ext='.parquet',
        ...     return_full_path=True
        ... )
    """
    import obstore as obs
    from obstore.store import S3Store

    if path is not None:
        bare_path = path.replace(f"s3://", "")
        bucket = bare_path.split("/")[0]
        prefix = bare_path.replace(bucket, "")[1:]
    elif bucket is not None and prefix is not None:
        pass
    else:
        raise ValueError("Either path or bucket and prefix must be provided")

    if credential_provider is None:
        credential_provider = s3_credential_provider()

    store = S3Store(
        bucket,
        region=region,
        config=config,
        client_options=client_options,
        retry_config=retry_config,
        credential_provider=credential_provider,
        skip_signature=skip_signature,
    )
    list_stream = obs.list(store, prefix=prefix)
    paths = []
    for batch in list_stream:
        for meta in batch:
            if return_full_path:
                paths.append(f"s3://{bucket}/{meta['path']}")
            else:
                paths.append(meta["path"])

    # Filter by file extension if provided
    if file_ext is not None:
        if not file_ext.startswith("."):
            file_ext = f".{file_ext}"
        paths = [path for path in paths if path.lower().endswith(file_ext.lower())]

    return paths


def s3_get_file(
    path: str,
    region: Optional[str] = None,
    config: Optional[dict] = None,
    client_options: Optional[dict] = None,
    retry_config: Optional[dict] = None,
    credential_provider: Optional["Boto3CredentialProvider"] = None,
    skip_signature: bool = True,
    output_file: Optional[str] = None,
    overwrite: bool = False,
) -> str:
    """Download a file from S3 to local filesystem.

    This function downloads a single file from an S3 bucket using the obstore library.
    It supports both full S3 URLs and relative paths, and can save the file to a
    specified location or the system's temporary directory.

    Args:
        path (str): The S3 path to the file. Can be either:
                   - Full S3 URL: 's3://bucket/path/to/file.ext'
                   - Relative path: 'bucket/path/to/file.ext' (s3:// will be added)
        region (str, optional): AWS region where the bucket is located.
                               If not provided, uses default region.
        config (dict, optional): Additional S3 configuration options.
        client_options (dict, optional): Additional S3 client options.
        retry_config (dict, optional): Retry configuration for S3 operations.
        credential_provider (Boto3CredentialProvider, optional): Custom credential provider.
                                                                If not provided, creates a default one.
        skip_signature (bool, optional): Whether to skip request signing for public buckets.
                                        Defaults to True.
        output_file (str, optional): Local path where the file should be saved.
                                    If not provided, saves to system temp directory
                                    with the original filename.
        overwrite (bool, optional): Whether to overwrite the file if it already exists.
                                    Defaults to False.


    Returns:
        The local path where the file was saved.

    Raises:
        FileNotFoundError: If the file does not exist in the S3 bucket.
        PermissionError: If there are insufficient permissions to write to the output location.
        ValueError: If the path format is invalid.

    Example:
        >>> # Download to temp directory
        >>> local_path = s3_get_file(
        ...     's3://my-bucket/data/file.parquet',
        ...     region='us-west-2'
        ... )

        >>> # Download to specific location
        >>> local_path = s3_get_file(
        ...     's3://my-bucket/data/file.parquet',
        ...     region='us-west-2',
        ...     output_file='/home/user/downloads/file.parquet'
        ... )

        >>> print(f"File downloaded to: {local_path}")
    """
    import tempfile

    import obstore as obs
    from obstore.store import S3Store

    if path.startswith("s3://"):
        pass
    else:
        path = f"s3://{path}"

    bucket = path.split("/")[2]
    filename = path.split("/")[-1]
    full_path = path.replace(f"s3://{bucket}/", "")

    if output_file is None:
        output_file = os.path.join(tempfile.gettempdir(), filename)

    if not overwrite and os.path.exists(output_file):
        print(f"File {output_file} already exists. Set overwrite=True to overwrite.")
        return output_file

    credential_provider = s3_credential_provider()
    store = S3Store(
        bucket,
        region=region,
        config=config,
        client_options=client_options,
        retry_config=retry_config,
        credential_provider=credential_provider,
        skip_signature=skip_signature,
    )

    resp = obs.get(store, full_path)

    with open(output_file, "wb") as f:
        for chunk in resp:
            f.write(chunk)

    return output_file


def read_raster(
    source,
    window=None,
    return_array=True,
    coord_crs=None,
    request_payer="bucket-owner",
    env_args={},
    open_args={},
    **kwargs: Any,
):
    """Read a raster from S3.

    Args:
        source (str): The path to the raster on S3.
        window (tuple, optional): The window (col_off, row_off, width, height) to read. Defaults to None.
        return_array (bool, optional): Whether to return a numpy array. Defaults to True.
        coord_crs (str, optional): The coordinate CRS of the input coordinates. Defaults to None.
        request_payer (str, optional): Specifies who pays for the download from S3.
            Can be "bucket-owner" or "requester". Defaults to "bucket-owner".
        env_args (dict, optional): Additional arguments to pass to rasterio.Env(). Defaults to {}.
        open_args (dict, optional): Additional arguments to pass to rasterio.open(). Defaults to {}.

    Returns:
        The raster as a numpy array.
    """
    import rasterio
    from rasterio.windows import Window

    with rasterio.Env(AWS_REQUEST_PAYER=request_payer, **env_args):
        src = rasterio.open(source, **open_args)
        if not return_array:
            return src
        else:
            if window is None:
                window = Window(0, 0, src.width, src.height)
            else:
                if isinstance(window, list):
                    coords = coords_to_xy(
                        source,
                        window,
                        coord_crs,
                        env_args=env_args,
                        open_args=open_args,
                    )
                    window = xy_to_window(coords)
                window = Window(*window)

            array = src.read(window=window, **kwargs)
            return array


def read_rasters(
    sources,
    window=None,
    coord_crs=None,
    request_payer="bucket-owner",
    env_args={},
    open_args={},
    **kwargs: Any,
):
    """Read a raster from S3.

    Args:
        sources (str): The list of paths to the raster files.
        window (tuple, optional): The window (col_off, row_off, width, height) to read. Defaults to None.
        coord_crs (str, optional): The coordinate CRS of the input coordinates. Defaults to None.
        request_payer (str, optional): Specifies who pays for the download from S3.
            Can be "bucket-owner" or "requester". Defaults to "bucket-owner".
        env_args (dict, optional): Additional arguments to pass to rasterio.Env(). Defaults to {}.
        open_args (dict, optional): Additional arguments to pass to rasterio.open(). Defaults to {}.

    Returns:
        The raster as a numpy array.
    """
    import numpy as np

    if not isinstance(sources, list):
        sources = [sources]

    array_list = []

    for source in sources:
        array = read_raster(
            source,
            window,
            True,
            coord_crs,
            request_payer,
            env_args,
            open_args,
            **kwargs,
        )
        array_list.append(array)

    result = np.concatenate(array_list, axis=0)
    return result


def transform_coords(x, y, src_crs, dst_crs, **kwargs: Any):
    """Transform coordinates from one CRS to another.

    Args:
        x (float): The x coordinate.
        y (float): The y coordinate.
        src_crs (str): The source CRS, e.g., "EPSG:4326".
        dst_crs (str): The destination CRS, e.g., "EPSG:3857".

    Returns:
        The transformed coordinates in the format of (x, y).
    """
    import pyproj

    transformer = pyproj.Transformer.from_crs(
        src_crs, dst_crs, always_xy=True, **kwargs
    )
    return transformer.transform(x, y)


def transform_bbox_coords(bbox, src_crs, dst_crs, **kwargs: Any):
    """Transforms the coordinates of a bounding box [x1, y1, x2, y2] from one CRS to another.

    Args:
        bbox (list | tuple): The bounding box [x1, y1, x2, y2] coordinates.
        src_crs (str): The source CRS, e.g., "EPSG:4326".
        dst_crs (str): The destination CRS, e.g., "EPSG:3857".

    Returns:
        The transformed bounding box [x1, y1, x2, y2] coordinates.
    """
    x1, y1, x2, y2 = bbox

    x1, y1 = transform_coords(
        x1, y1, src_crs, dst_crs, **kwargs
    )  # pylint: disable=E0633
    x2, y2 = transform_coords(
        x2, y2, src_crs, dst_crs, **kwargs
    )  # pylint: disable=E0633

    return [x1, y1, x2, y2]


def coords_to_xy(
    src_fp: str,
    coords: list,
    coord_crs: str = "epsg:4326",
    request_payer="bucket-owner",
    env_args={},
    open_args={},
    **kwargs: Any,
) -> list:
    """Converts a list of coordinates to pixel coordinates, i.e., (col, row) coordinates.

    Args:
        src_fp (str): The source raster file path.
        coords (list): A list of coordinates in the format of [[x1, y1], [x2, y2], ...]
        coord_crs (str): The coordinate CRS of the input coordinates. Defaults to "epsg:4326".
        request_payer (str): Specifies who pays for the download from S3.
            Can be "bucket-owner" or "requester". Defaults to "bucket-owner".
        env_args (dict): Additional keyword arguments to pass to rasterio.Env.
        open_args (dict): Additional keyword arguments to pass to rasterio.open.
        **kwargs (Any): Additional keyword arguments to pass to rasterio.transform.rowcol.

    Returns:
        A list of pixel coordinates in the format of [[x1, y1], [x2, y2], ...]
    """
    import numpy as np
    import rasterio

    if isinstance(coords, np.ndarray):
        coords = coords.tolist()

    if len(coords) == 4 and all([isinstance(c, (int, float)) for c in coords]):
        coords = [[coords[0], coords[1]], [coords[2], coords[3]]]

    xs, ys = zip(*coords)
    with rasterio.Env(AWS_REQUEST_PAYER=request_payer, **env_args):
        with rasterio.open(src_fp, **open_args) as src:
            width = src.width
            height = src.height
            if coord_crs != src.crs:
                xs, ys = transform_coords(
                    xs, ys, coord_crs, src.crs, **kwargs
                )  # pylint: disable=E0633
            rows, cols = rasterio.transform.rowcol(src.transform, xs, ys, **kwargs)
        result = [[col, row] for col, row in zip(cols, rows)]

        result = [
            [x, y] for x, y in result if x >= 0 and y >= 0 and x < width and y < height
        ]
        if len(result) == 0:
            print("No valid pixel coordinates found.")
        elif len(result) < len(coords):
            print("Some coordinates are out of the image boundary.")

        return result


def xy_to_window(xy) -> Tuple[float, float, float, float]:
    """Converts a list of coordinates to a rasterio window.

    Args:
        xy (list): A list of coordinates in the format of [[x1, y1], [x2, y2]]

    Returns:
        The rasterio window in the format of (col_off, row_off, width, height).
    """

    x1, y1 = xy[0]
    x2, y2 = xy[1]

    left = min(x1, x2)
    right = max(x1, x2)
    top = min(y1, y2)
    bottom = max(y1, y2)

    width = right - left
    height = bottom - top

    return (left, top, width, height)


def map_tiles_to_geotiff(
    output,
    bbox,
    zoom=None,
    resolution=None,
    source="OpenStreetMap",
    crs="EPSG:3857",
    to_cog=False,
    quiet=False,
    **kwargs: Any,
):
    """Download map tiles and convert them to a GeoTIFF. The source is adapted from https://github.com/gumblex/tms2geotiff.
        Credits to the GitHub user @gumblex.

    Args:
        output (str): The output GeoTIFF file.
        bbox (list): The bounding box [minx, miny, maxx, maxy] coordinates in EPSG:4326, e.g., [-122.5216, 37.733, -122.3661, 37.8095]
        zoom (int, optional): The map zoom level. Defaults to None.
        resolution (float, optional): The resolution in meters. Defaults to None.
        source (str, optional): The tile source. It can be one of the following: "OPENSTREETMAP", "ROADMAP",
            "SATELLITE", "TERRAIN", "HYBRID", or an HTTP URL. Defaults to "OpenStreetMap".
        crs (str, optional): The coordinate reference system. Defaults to "EPSG:3857".
        to_cog (bool, optional): Convert to Cloud Optimized GeoTIFF. Defaults to False.
        quiet (bool, optional): Suppress output. Defaults to False.
        **kwargs (Any): Additional arguments to pass to gdal.GetDriverByName("GTiff").Create().

    """
    import concurrent.futures
    import io
    import itertools
    import math
    import re

    import numpy
    from PIL import Image

    try:
        from osgeo import gdal, osr
    except ImportError:
        raise ImportError("GDAL is not installed. Install it with pip install GDAL")

    try:
        import httpx

        SESSION = httpx.Client()
    except ImportError:
        import requests

        SESSION = requests.Session()

    SESSION.headers.update(
        {
            "Accept": "*/*",
            "Accept-Encoding": "gzip, deflate",
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; rv:91.0) Gecko/20100101 Firefox/91.0",
        }
    )

    xyz_tiles = {
        "OPENSTREETMAP": {
            "url": "https://tile.openstreetmap.org/{z}/{x}/{y}.png",
            "attribution": "OpenStreetMap",
            "name": "OpenStreetMap",
        },
        "ROADMAP": {
            "url": "https://mt1.google.com/vt/lyrs=m&x={x}&y={y}&z={z}",
            "attribution": "Google",
            "name": "Google Maps",
        },
        "SATELLITE": {
            "url": "https://mt1.google.com/vt/lyrs=s&x={x}&y={y}&z={z}",
            "attribution": "Google",
            "name": "Google Satellite",
        },
        "TERRAIN": {
            "url": "https://mt1.google.com/vt/lyrs=p&x={x}&y={y}&z={z}",
            "attribution": "Google",
            "name": "Google Terrain",
        },
        "HYBRID": {
            "url": "https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}",
            "attribution": "Google",
            "name": "Google Satellite",
        },
    }

    if isinstance(source, str) and source.upper() in xyz_tiles:
        source = xyz_tiles[source.upper()]["url"]
    elif isinstance(source, str) and source.startswith("http"):
        pass
    elif isinstance(source, str):
        tiles = basemap_xyz_tiles()
        if source in tiles:
            source = tiles[source].url
    else:
        raise ValueError(
            'source must be one of "OpenStreetMap", "ROADMAP", "SATELLITE", "TERRAIN", "HYBRID", or a URL'
        )

    # Web Mercator tile size in meters at zoom level 0
    MERCATOR_ZOOM_0_RESOLUTION_M = 156543.03392804097

    def resolution_to_zoom_level(resolution):
        """
        Convert map resolution in meters to zoom level for Web Mercator (EPSG:3857) tiles.
        """
        initial_resolution = MERCATOR_ZOOM_0_RESOLUTION_M

        # Calculate the zoom level
        zoom_level = math.log2(initial_resolution / resolution)

        return int(zoom_level)

    def zoom_level_to_resolution(zoom):
        """
        Convert map zoom level to resolution in meters for Web Mercator (EPSG:3857) tiles.
        """
        initial_resolution = MERCATOR_ZOOM_0_RESOLUTION_M

        # Calculate resolution
        resolution_m = initial_resolution / (2**zoom)

        return resolution_m

    if isinstance(bbox, list) and len(bbox) == 4:
        west, south, east, north = bbox
    else:
        raise ValueError(
            "bbox must be a list of 4 coordinates in the format of [xmin, ymin, xmax, ymax]"
        )

    if (zoom is None) and (resolution is None):
        raise ValueError("Either zoom or resolution must be provided")

    elif (zoom is not None) and (resolution is not None):
        raise ValueError("Only one of zoom or resolution can be provided")

    elif (zoom is None) and (resolution is not None):
        zoom = resolution_to_zoom_level(resolution)
    else:
        # condition: (resolution is None) and (zoom is not None):
        resolution = zoom_level_to_resolution(zoom)

    EARTH_EQUATORIAL_RADIUS = 6378137.0

    Image.MAX_IMAGE_PIXELS = None

    gdal.UseExceptions()
    web_mercator = osr.SpatialReference()
    try:
        web_mercator.ImportFromEPSG(3857)
    except RuntimeError as e:
        # https://github.com/PDAL/PDAL/issues/2544#issuecomment-637995923
        if "PROJ" in str(e):
            pattern = r"/[\w/]+"
            match = re.search(pattern, str(e))
            if match:
                file_path = match.group(0)
                os.environ["PROJ_LIB"] = file_path
                os.environ["GDAL_DATA"] = file_path.replace("proj", "gdal")
                web_mercator.ImportFromEPSG(3857)

    WKT_3857 = web_mercator.ExportToWkt()

    def from4326_to3857(lat, lon):
        xtile = math.radians(lon) * EARTH_EQUATORIAL_RADIUS
        ytile = (
            math.log(math.tan(math.radians(45 + lat / 2.0))) * EARTH_EQUATORIAL_RADIUS
        )
        return (xtile, ytile)

    def deg2num(lat, lon, zoom):
        lat_r = math.radians(lat)
        n = 2**zoom
        xtile = (lon + 180) / 360 * n
        ytile = (1 - math.log(math.tan(lat_r) + 1 / math.cos(lat_r)) / math.pi) / 2 * n
        return (xtile, ytile)

    def is_empty(im):
        extrema = im.getextrema()
        if len(extrema) >= 3:
            if len(extrema) > 3 and extrema[-1] == (0, 0):
                return True
            for ext in extrema[:3]:
                if ext != (0, 0):
                    return False
            return True
        else:
            return extrema[0] == (0, 0)

    def paste_tile(bigim, base_size, tile, corner_xy, bbox):
        if tile is None:
            return bigim
        im = Image.open(io.BytesIO(tile))
        mode = "RGB" if im.mode == "RGB" else "RGBA"
        size = im.size
        if bigim is None:
            base_size[0] = size[0]
            base_size[1] = size[1]
            newim = Image.new(
                mode, (size[0] * (bbox[2] - bbox[0]), size[1] * (bbox[3] - bbox[1]))
            )
        else:
            newim = bigim

        dx = abs(corner_xy[0] - bbox[0])
        dy = abs(corner_xy[1] - bbox[1])
        xy0 = (size[0] * dx, size[1] * dy)
        if mode == "RGB":
            newim.paste(im, xy0)
        else:
            if im.mode != mode:
                im = im.convert(mode)
            if not is_empty(im):
                newim.paste(im, xy0)
        im.close()
        return newim

    def finish_picture(bigim, base_size, bbox, x0, y0, x1, y1):
        xfrac = x0 - bbox[0]
        yfrac = y0 - bbox[1]
        x2 = round(base_size[0] * xfrac)
        y2 = round(base_size[1] * yfrac)
        imgw = round(base_size[0] * (x1 - x0))
        imgh = round(base_size[1] * (y1 - y0))
        retim = bigim.crop((x2, y2, x2 + imgw, y2 + imgh))
        if retim.mode == "RGBA" and retim.getextrema()[3] == (255, 255):
            retim = retim.convert("RGB")
        bigim.close()
        return retim

    def get_tile(url):
        retry = 3
        while 1:
            try:
                r = SESSION.get(url, timeout=60)
                break
            except Exception:
                retry -= 1
                if not retry:
                    raise
        if r.status_code == 404:
            return None
        elif not r.content:
            return None
        r.raise_for_status()
        return r.content

    def draw_tile(
        source, lat0, lon0, lat1, lon1, zoom, filename, quiet=False, **kwargs
    ):
        x0, y0 = deg2num(lat0, lon0, zoom)
        x1, y1 = deg2num(lat1, lon1, zoom)
        x0, x1 = sorted([x0, x1])
        y0, y1 = sorted([y0, y1])
        corners = tuple(
            itertools.product(
                range(math.floor(x0), math.ceil(x1)),
                range(math.floor(y0), math.ceil(y1)),
            )
        )
        totalnum = len(corners)
        futures = []
        with concurrent.futures.ThreadPoolExecutor(5) as executor:
            for x, y in corners:
                futures.append(
                    executor.submit(get_tile, source.format(z=zoom, x=x, y=y))
                )
            bbox = (math.floor(x0), math.floor(y0), math.ceil(x1), math.ceil(y1))
            bigim = None
            base_size = [256, 256]
            for k, (fut, corner_xy) in enumerate(zip(futures, corners), 1):
                bigim = paste_tile(bigim, base_size, fut.result(), corner_xy, bbox)
                if not quiet:
                    print("Downloaded image %d/%d" % (k, totalnum))

        if not quiet:
            print("Saving GeoTIFF. Please wait...")
        img = finish_picture(bigim, base_size, bbox, x0, y0, x1, y1)
        imgbands = len(img.getbands())
        driver = gdal.GetDriverByName("GTiff")

        if "options" not in kwargs:
            kwargs["options"] = [
                "COMPRESS=DEFLATE",
                "PREDICTOR=2",
                "ZLEVEL=9",
                "TILED=YES",
            ]

        kwargs.pop("overwrite", None)
        gtiff = driver.Create(
            filename,
            img.size[0],
            img.size[1],
            imgbands,
            gdal.GDT_Byte,
            **kwargs,
        )

        gtiff.SetMetadata({"ZOOM_LEVEL": str(zoom), "RESOLUTION_M": str(resolution)})

        xp0, yp0 = from4326_to3857(lat0, lon0)
        xp1, yp1 = from4326_to3857(lat1, lon1)
        pwidth = abs(xp1 - xp0) / img.size[0]
        pheight = abs(yp1 - yp0) / img.size[1]
        gtiff.SetGeoTransform((min(xp0, xp1), pwidth, 0, max(yp0, yp1), 0, -pheight))
        gtiff.SetProjection(WKT_3857)
        for band in range(imgbands):
            array = np.array(img.getdata(band), dtype="u8")
            array = array.reshape((img.size[1], img.size[0]))
            band = gtiff.GetRasterBand(band + 1)
            band.WriteArray(array)
        gtiff.FlushCache()

        if not quiet:
            print(f"Image saved to {filename}")
        return img

    try:
        draw_tile(source, south, west, north, east, zoom, output, quiet, **kwargs)
        if crs.upper() != "EPSG:3857":
            reproject(image=output, output=output, dst_crs=crs, to_cog=to_cog)
        elif to_cog:
            image_to_cog(source=output, dst_path=output)
    except Exception as e:
        raise Exception(e)


tms_to_geotiff = map_tiles_to_geotiff


def tif_to_jp2(filename, output, creationOptions=None):
    """Converts a GeoTIFF to JPEG2000.

    Args:
        filename (str): The path to the GeoTIFF file.
        output (str): The path to the output JPEG2000 file.
        creationOptions (list): A list of creation options for the JPEG2000 file. See
            https://gdal.org/drivers/raster/jp2openjpeg.html. For example, to specify the compression
            ratio, use ``["QUALITY=20"]``. A value of 20 means the file will be 20% of the size in comparison
            to uncompressed data.

    """

    if not os.path.exists(filename):
        raise Exception(f"File {filename} does not exist")

    if not output.endswith(".jp2"):
        output += ".jp2"

    from osgeo import gdal

    in_ds = gdal.Open(filename)
    gdal.Translate(output, in_ds, format="JP2OpenJPEG", creationOptions=creationOptions)
    in_ds = None


def image_comparison(
    img1: str,
    img2: str,
    label1: str = "1",
    label2: str = "2",
    width: int = 704,
    show_labels: bool = True,
    starting_position: int = 50,
    make_responsive: bool = True,
    in_memory: bool = True,
    out_html: str = None,
):
    """Create a comparison slider for two images. The source code is adapted from
        https://github.com/fcakyon/streamlit-image-comparison. Credits to the GitHub user @fcakyon.
        Users can also use https://juxtapose.knightlab.com to create a comparison slider.

    Args:
        img1 (str): Path to the first image. It can be a local file path, a URL, or a numpy array.
        img2 (str): Path to the second image. It can be a local file path, a URL, or a numpy array.
        label1 (str, optional): Label for the first image. Defaults to "1".
        label2 (str, optional): Label for the second image. Defaults to "2".
        width (int, optional): Width of the component in pixels. Defaults to 704.
        show_labels (bool, optional): Whether to show labels on the images. Default is True.
        starting_position (int, optional): Starting position of the slider as a percentage (0-100). Default is 50.
        make_responsive (bool, optional): Whether to enable responsive mode. Default is True.
        in_memory (bool, optional): Whether to handle pillow to base64 conversion in memory without saving to local. Default is True.
        out_html (str, optional): Whether to handle pillow to base64 conversion in memory without saving to local. Default is True.

    """

    import base64
    import io
    import os
    import tempfile
    import uuid
    from typing import Union

    import numpy as np
    import requests
    from IPython.display import HTML, display
    from PIL import Image

    TEMP_DIR = os.path.join(tempfile.gettempdir(), random_string(6))
    os.makedirs(TEMP_DIR, exist_ok=True)

    def exif_transpose(image: Image.Image):
        """
        Transpose a PIL image accordingly if it has an EXIF Orientation tag.
        Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose()
        :param image: The image to transpose.
        :return: An image.
        """
        exif = image.getexif()
        orientation = exif.get(0x0112, 1)  # default 1
        if orientation > 1:
            method = {
                2: Image.FLIP_LEFT_RIGHT,
                3: Image.ROTATE_180,
                4: Image.FLIP_TOP_BOTTOM,
                5: Image.TRANSPOSE,
                6: Image.ROTATE_270,
                7: Image.TRANSVERSE,
                8: Image.ROTATE_90,
            }.get(orientation)
            if method is not None:
                image = image.transpose(method)
                del exif[0x0112]
                image.info["exif"] = exif.tobytes()
        return image

    def read_image_as_pil(
        image: Union[Image.Image, str, np.ndarray], exif_fix: bool = False
    ):
        """
        Loads an image as PIL.Image.Image.
        Args:
            image : Can be image path or url (str), numpy image (np.ndarray) or PIL.Image
        """
        # https://stackoverflow.com/questions/56174099/how-to-load-images-larger-than-max-image-pixels-with-pil
        Image.MAX_IMAGE_PIXELS = None

        if isinstance(image, Image.Image):
            image_pil = image.convert("RGB")
        elif isinstance(image, str):
            # read image if str image path is provided
            try:
                image_pil = Image.open(
                    requests.get(image, stream=True).raw
                    if str(image).startswith("http")
                    else image
                ).convert("RGB")
                if exif_fix:
                    image_pil = exif_transpose(image_pil)
            except:  # handle large/tiff image reading
                try:
                    import skimage.io
                except ImportError:
                    raise ImportError(
                        "Please run 'pip install -U scikit-image imagecodecs' for large image handling."
                    )
                image_sk = skimage.io.imread(image).astype(np.uint8)
                if len(image_sk.shape) == 2:  # b&w
                    image_pil = Image.fromarray(image_sk, mode="1").convert("RGB")
                elif image_sk.shape[2] == 4:  # rgba
                    image_pil = Image.fromarray(image_sk, mode="RGBA").convert("RGB")
                elif image_sk.shape[2] == 3:  # rgb
                    image_pil = Image.fromarray(image_sk, mode="RGB")
                else:
                    raise TypeError(
                        f"image with shape: {image_sk.shape[3]} is not supported."
                    )
        elif isinstance(image, np.ndarray):
            if image.shape[0] < 5:  # image in CHW
                image = image[:, :, ::-1]
            image_pil = Image.fromarray(image).convert("RGB")
        else:
            raise TypeError("read image with 'pillow' using 'Image.open()'")

        return image_pil

    def pillow_to_base64(image: Image.Image) -> str:
        """
        Convert a PIL image to a base64-encoded string.

        Parameters
        ----------
        image: PIL.Image.Image
            The image to be converted.

        Returns
        -------
        str
            The base64-encoded string.
        """
        in_mem_file = io.BytesIO()
        image.save(in_mem_file, format="JPEG", subsampling=0, quality=100)
        img_bytes = in_mem_file.getvalue()  # bytes
        image_str = base64.b64encode(img_bytes).decode("utf-8")
        base64_src = f"data:image/jpg;base64,{image_str}"
        return base64_src

    def local_file_to_base64(image_path: str) -> str:
        """
        Convert a local image file to a base64-encoded string.

        Parameters
        ----------
        image_path: str
            The path to the image file.

        Returns
        -------
        str
            The base64-encoded string.
        """
        file_ = open(image_path, "rb")
        img_bytes = file_.read()
        image_str = base64.b64encode(img_bytes).decode("utf-8")
        file_.close()
        base64_src = f"data:image/jpg;base64,{image_str}"
        return base64_src

    def pillow_local_file_to_base64(image: Image.Image, temp_dir: str):
        """
        Convert a Pillow image to a base64 string, using a temporary file on disk.

        Parameters
        ----------
        image : PIL.Image.Image
            The Pillow image to convert.
        temp_dir : str
            The directory to use for the temporary file.

        Returns
        -------
        str
            A base64-encoded string representing the image.
        """
        # Create temporary file path using os.path.join()
        img_path = os.path.join(temp_dir, str(uuid.uuid4()) + ".jpg")

        # Save image to temporary file
        image.save(img_path, subsampling=0, quality=100)

        # Convert temporary file to base64 string
        base64_src = local_file_to_base64(img_path)

        return base64_src

    # Prepare images
    img1_pillow = read_image_as_pil(img1)
    img2_pillow = read_image_as_pil(img2)

    img_width, img_height = img1_pillow.size
    h_to_w = img_height / img_width
    height = int((width * h_to_w) * 0.95)

    if in_memory:
        # Convert images to base64 strings
        img1 = pillow_to_base64(img1_pillow)
        img2 = pillow_to_base64(img2_pillow)
    else:
        # Create base64 strings from temporary files
        os.makedirs(TEMP_DIR, exist_ok=True)
        for file_ in os.listdir(TEMP_DIR):
            if file_.endswith(".jpg"):
                os.remove(os.path.join(TEMP_DIR, file_))
        img1 = pillow_local_file_to_base64(img1_pillow, TEMP_DIR)
        img2 = pillow_local_file_to_base64(img2_pillow, TEMP_DIR)

    # Load CSS and JS
    cdn_path = "https://cdn.knightlab.com/libs/juxtapose/latest"
    css_block = f'<link rel="stylesheet" href="{cdn_path}/css/juxtapose.css">'
    js_block = f'<script src="{cdn_path}/js/juxtapose.min.js"></script>'

    # write html block
    htmlcode = f"""
        <html>
        <head>
        <style>body {{ margin: unset; }}</style>
        {css_block}
        {js_block}
        <div id="foo" style="height: {height}; width: {width or '100%'};"></div>
        <script>
        slider = new juxtapose.JXSlider('#foo',
            [
                {{
                    src: '{img1}',
                    label: '{label1}',
                }},
                {{
                    src: '{img2}',
                    label: '{label2}',
                }}
            ],
            {{
                animate: true,
                showLabels: {'true' if show_labels else 'false'},
                showCredits: true,
                startingPosition: "{starting_position}%",
                makeResponsive: {'true' if make_responsive else 'false'},
            }});
        </script>
        </head>
        </html>
        """

    if out_html is not None:
        with open(out_html, "w") as f:
            f.write(htmlcode)

    shutil.rmtree(TEMP_DIR)

    display(HTML(htmlcode))


def get_nhd_basins(
    feature_ids,
    fsource="nwissite",
    split_catchment=False,
    simplified=True,
    **kwargs: Any,
):
    """Get NHD basins for a list of station IDs.

    Args:
        feature_ids (str | list): Target feature ID(s).
        fsource (str, optional): The name of feature(s) source, defaults to ``nwissite``.
            The valid sources are:
            * 'comid' for NHDPlus comid.
            * 'ca_gages' for Streamgage catalog for CA SB19
            * 'gfv11_pois' for USGS Geospatial Fabric V1.1 Points of Interest
            * 'huc12pp' for HUC12 Pour Points
            * 'nmwdi-st' for New Mexico Water Data Initiative Sites
            * 'nwisgw' for NWIS Groundwater Sites
            * 'nwissite' for NWIS Surface Water Sites
            * 'ref_gage' for geoconnex.us reference gauges
            * 'vigil' for Vigil Network Data
            * 'wade' for Water Data Exchange 2.0 Sites
            * 'WQP' for Water Quality Portal
        split_catchment (bool, optional): If True, split basins at their outlet locations
        simplified (bool, optional): If True, return a simplified version of basin geometries.
            Default to True.

    Raises:
        ImportError: If pynhd is not installed.

    Returns:
        NLDI indexed basins in EPSG:4326. If some IDs don't return any features
            a list of missing ID(s) are returned as well.
    """

    try:
        from pynhd import NLDI
    except ImportError:
        raise ImportError("pynhd is not installed. Install it with pip install pynhd")

    return NLDI().get_basins(
        feature_ids, fsource, split_catchment, simplified, **kwargs
    )


def get_3dep_dem(
    geometry,
    resolution=30,
    src_crs=None,
    output=None,
    dst_crs="EPSG:5070",
    to_cog=False,
    overwrite=False,
    **kwargs: Any,
):
    """Get DEM data at any resolution from 3DEP.

    Args:
        geometry (Polygon | MultiPolygon | tuple): It can be a polygon or a bounding
            box of form (xmin, ymin, xmax, ymax).
        resolution (int): arget DEM source resolution in meters. Defaults to 30.
        src_crs (str, optional): The spatial reference system of the input geometry. Defaults to "EPSG:4326".
        output (str, optional): The output GeoTIFF file. Defaults to None.
        dst_crs (str, optional): The spatial reference system of the output GeoTIFF file. Defaults to "EPSG:5070".
        to_cog (bool, optional): Convert to Cloud Optimized GeoTIFF. Defaults to False.
        overwrite (bool, optional): Whether to overwrite the output file if it exists. Defaults to False.

    Returns:
        DEM at the specified resolution in meters and CRS as an xarray.DataArray.
    """

    try:
        import py3dep
    except ImportError:
        print("py3dep is not installed. Installing py3dep...")
        install_package("py3dep")
        import py3dep

    import geopandas as gpd

    if output is not None and os.path.exists(output) and not overwrite:
        print(f"File {output} already exists. Set overwrite=True to overwrite it")
        return

    if isinstance(geometry, gpd.GeoDataFrame):
        if src_crs is None:
            src_crs = geometry.crs
        geometry = geometry.geometry.union_all()

    if src_crs is None:
        src_crs = "EPSG:4326"

    dem = py3dep.get_dem(geometry, resolution=resolution, crs=src_crs)
    dem = dem.rio.reproject(dst_crs)

    if output is not None:
        if not output.endswith(".tif"):
            output += ".tif"
        dem.rio.to_raster(output, **kwargs)

        if to_cog:
            try:
                image_to_cog(output, output)
            except Exception as e:
                print(e)

    return dem


def vector_set_crs(source, output=None, crs="EPSG:4326", **kwargs: Any):
    """Set CRS of a vector file.

    Args:
        source (str | gpd.GeoDataFrame): The path to the vector file or a GeoDataFrame.
        output (str, optional): The path to the output vector file. Defaults to None.
        crs (str, optional): The CRS to set. Defaults to "EPSG:4326".


    Returns:
        The GeoDataFrame with the new CRS.
    """

    import geopandas as gpd

    if isinstance(source, str):
        source = gpd.read_file(source, **kwargs)

    if not isinstance(source, gpd.GeoDataFrame):
        raise TypeError("source must be a GeoDataFrame or a file path")

    gdf = source.set_crs(crs)

    if output is not None:
        gdf.to_file(output)
    else:
        return gdf


def select_largest(source, column, count=1, output=None, **kwargs: Any):
    """Select the largest features in a GeoDataFrame based on a column.

    Args:
        source (str | gpd.GeoDataFrame): The path to the vector file or a GeoDataFrame.
        column (str): The column to sort by.
        count (int, optional): The number of features to select. Defaults to 1.
        output (str, optional): The path to the output vector file. Defaults to None.

    Returns:
        The path to the output vector file.
    """

    import geopandas as gpd

    if isinstance(source, str):
        gdf = gpd.read_file(source, **kwargs)
    else:
        gdf = source

    if not isinstance(gdf, gpd.GeoDataFrame):
        raise TypeError("source must be a GeoDataFrame or a file path")

    gdf = gdf.sort_values(column, ascending=False).head(count)

    if output is not None:
        gdf.to_file(output)

    else:
        return gdf


def coords_to_vector(coords, output=None, crs="EPSG:4326", **kwargs: Any) -> Any:
    """Convert a list of coordinates to a GeoDataFrame or a vector file.

    Args:
        coords (list): A list of coordinates in the format of [(x1, y1), (x2, y2), ...].
        output (str, optional): The path to the output vector file. Defaults to None.
        crs (str, optional): The CRS of the coordinates. Defaults to "EPSG:4326".

    Returns:
        A GeoDataFrame of the coordinates.
    """
    import geopandas as gpd
    from shapely.geometry import Point

    if not isinstance(coords, (list, tuple)):
        raise TypeError("coords must be a list of coordinates")

    if isinstance(coords[0], int) or isinstance(coords[0], float):
        coords = [(coords[0], coords[1])]

    # convert the points to a GeoDataFrame
    geometry = [Point(xy) for xy in coords]
    gdf = gpd.GeoDataFrame(geometry=geometry, crs="EPSG:4326")
    gdf.to_crs(crs, inplace=True)

    if output is not None:
        gdf.to_file(output, **kwargs)
    else:
        return gdf


def check_html_string(html_string) -> str:
    """Check if an HTML string contains local images and convert them to base64.

    Args:
        html_string (str): The HTML string.

    Returns:
        The HTML string with local images converted to base64.
    """
    import base64
    import re

    # Search for img tags with src attribute
    img_regex = r'<img[^>]+src\s*=\s*["\']([^"\':]+)["\'][^>]*>'

    for match in re.findall(img_regex, html_string):
        with open(match, "rb") as img_file:
            img_data = img_file.read()
            base64_data = base64.b64encode(img_data).decode("utf-8")
            html_string = html_string.replace(
                'src="{}"'.format(match),
                'src="data:image/png;base64,' + base64_data + '"',
            )

    return html_string


def split_raster(filename, out_dir, tile_size=256, overlap=0, prefix="tile"):
    """Split a raster into tiles.

    Args:
        filename (str): The path or http URL to the raster file.
        out_dir (str): The path to the output directory.
        tile_size (int | tuple, optional): The size of the tiles. Can be an integer or a tuple of (width, height). Defaults to 256.
        overlap (int, optional): The number of pixels to overlap between tiles. Defaults to 0.
        prefix (str, optional): The prefix of the output tiles. Defaults to "tile".

    Raises:
        ImportError: Raised if GDAL is not installed.
    """

    try:
        from osgeo import gdal
    except ImportError:
        raise ImportError(
            "GDAL is required to use this function. Install it with `conda install gdal -c conda-forge`"
        )

    if isinstance(filename, str):
        if filename.startswith("http"):
            output = filename.split("/")[-1]
            download_file(filename, output)
            filename = output

    # Open the input GeoTIFF file
    ds = gdal.Open(filename)

    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    if isinstance(tile_size, int):
        tile_width = tile_size
        tile_height = tile_size
    elif isinstance(tile_size, tuple):
        tile_width = tile_size[0]
        tile_height = tile_size[1]
    else:
        raise ValueError("tile_size must be an integer or a tuple of (width, height)")

    # Get the size of the input raster
    width = ds.RasterXSize
    height = ds.RasterYSize

    # Calculate the number of tiles needed in both directions, taking into account the overlap
    num_tiles_x = (width - overlap) // (tile_width - overlap) + int(
        (width - overlap) % (tile_width - overlap) > 0
    )
    num_tiles_y = (height - overlap) // (tile_height - overlap) + int(
        (height - overlap) % (tile_height - overlap) > 0
    )

    # Get the georeferencing information of the input raster
    geotransform = ds.GetGeoTransform()

    # Loop over all the tiles
    for i in range(num_tiles_x):
        for j in range(num_tiles_y):
            # Calculate the pixel coordinates of the tile, taking into account the overlap and clamping to the edge of the raster
            x_min = i * (tile_width - overlap)
            y_min = j * (tile_height - overlap)
            x_max = min(x_min + tile_width, width)
            y_max = min(y_min + tile_height, height)

            # Adjust the size of the last tile in each row and column to include any remaining pixels
            if i == num_tiles_x - 1:
                x_min = max(x_max - tile_width, 0)
            if j == num_tiles_y - 1:
                y_min = max(y_max - tile_height, 0)

            # Calculate the size of the tile, taking into account the overlap
            tile_width = x_max - x_min
            tile_height = y_max - y_min

            # Set the output file name
            output_file = f"{out_dir}/{prefix}_{i}_{j}.tif"

            # Create a new dataset for the tile
            driver = gdal.GetDriverByName("GTiff")
            tile_ds = driver.Create(
                output_file,
                tile_width,
                tile_height,
                ds.RasterCount,
                ds.GetRasterBand(1).DataType,
            )

            # Calculate the georeferencing information for the output tile
            tile_geotransform = (
                geotransform[0] + x_min * geotransform[1],
                geotransform[1],
                0,
                geotransform[3] + y_min * geotransform[5],
                0,
                geotransform[5],
            )

            # Set the geotransform and projection of the tile
            tile_ds.SetGeoTransform(tile_geotransform)
            tile_ds.SetProjection(ds.GetProjection())

            # Read the data from the input raster band(s) and write it to the tile band(s)
            for k in range(ds.RasterCount):
                band = ds.GetRasterBand(k + 1)
                tile_band = tile_ds.GetRasterBand(k + 1)
                tile_data = band.ReadAsArray(x_min, y_min, tile_width, tile_height)
                tile_band.WriteArray(tile_data)

            # Close the tile dataset
            tile_ds = None

    # Close the input dataset
    ds = None


def merge_rasters(
    input_dir_or_files: Union[str, List[str]],
    output: str,
    input_pattern: str = "*.tif",
    output_format: str = "GTiff",
    output_nodata: float = None,
    output_options: list = None,
    **kwargs: Any,
):
    """
    Merge a directory of rasters or a list of file paths into a single raster.

    Args:
        input_dir_or_files (Union[str, List[str]]): The path to the input directory or a list of file paths.
        output (str): The path to the output raster.
        input_pattern (str, optional): The glob pattern to match input files if a directory is provided. Defaults to "*.tif".
        output_format (str, optional): The output raster format. Defaults to "GTiff".
        output_nodata (float, optional): The nodata value for the output raster. Defaults to None.
        output_options (list, optional): A list of creation options for the output raster. Defaults to ["COMPRESS=DEFLATE"].
        **kwargs (Any): Additional arguments to pass to gdal.WarpOptions.

    Raises:
        ImportError: If GDAL is not installed.
        ValueError: If no input files are found.
    """
    import glob

    if output_options is None:
        output_options = ["COMPRESS=DEFLATE"]

    try:
        from osgeo import gdal  # Ensure GDAL is available
    except ImportError as e:
        raise ImportError(
            "GDAL is required to use this function. Install it with `conda install gdal -c conda-forge`"
        ) from e

    # Get a list of input files
    if isinstance(input_dir_or_files, str):  # It's a directory
        input_files = glob.glob(os.path.join(input_dir_or_files, input_pattern))
    elif isinstance(input_dir_or_files, list):  # It's a list of file paths
        input_files = input_dir_or_files
    else:
        raise ValueError(
            "input_dir_or_files must be a directory path (str) or a list of file paths (List[str])."
        )

    # Check if files are found
    if not input_files:
        raise ValueError(
            "No input files found. Please check the directory or file paths provided."
        )

    # Configure warp options
    warp_options = gdal.WarpOptions(
        format=output_format,
        dstNodata=output_nodata,
        creationOptions=output_options,
        **kwargs,
    )

    # Perform the raster merge
    print(f"Merging {len(input_files)} rasters into '{output}'...")
    result = gdal.Warp(
        destNameOrDestDS=output,
        srcDSOrSrcDSTab=input_files,
        options=warp_options,
    )

    # Check for success
    if result is None:
        raise RuntimeError(
            "Raster merge failed. Please check your inputs and parameters."
        )
    print(f"Raster merge completed successfully: {output}")


def get_geometry_type(in_geojson: Union[str, Dict]) -> str:
    """Get the geometry type of a GeoJSON file.

    Args:
        in_geojson (str | dict): The path to the GeoJSON file or a GeoJSON dictionary.

    Returns:
        The geometry type. Can be one of "Point", "LineString", "Polygon", "MultiPoint",
            "MultiLineString", "MultiPolygon", "GeometryCollection", or "Unknown".
    """

    import geojson

    try:
        if isinstance(in_geojson, str):  # If input is a file path
            with open(in_geojson, "r") as geojson_file:
                geojson_data = geojson.load(geojson_file)
        elif isinstance(in_geojson, dict):  # If input is a GeoJSON dictionary
            geojson_data = in_geojson
        else:
            return "Invalid input type. Expected file path or dictionary."

        if "type" in geojson_data:
            if geojson_data["type"] == "FeatureCollection":
                features = geojson_data.get("features", [])
                if features:
                    first_feature = features[0]
                    geometry = first_feature.get("geometry")
                    if geometry and "type" in geometry:
                        return geometry["type"]
                    else:
                        return "No geometry type found in the first feature."
                else:
                    return "No features found in the FeatureCollection."
            elif geojson_data["type"] == "Feature":
                geometry = geojson_data.get("geometry")
                if geometry and "type" in geometry:
                    return geometry["type"]
                else:
                    return "No geometry type found in the Feature."
            else:
                return "Unsupported GeoJSON type."
        else:
            return "No 'type' field found in the GeoJSON data."
    except Exception as e:
        raise e


def get_google_map(
    map_type="HYBRID", show=True, api_key=None, backend="ipyleaflet", **kwargs
):
    """Gets Google basemap tile layer.

    Args:
        map_type (str, optional): Can be one of "ROADMAP", "SATELLITE", "HYBRID" or "TERRAIN". Defaults to 'HYBRID'.
        show (bool, optional): Whether to add the layer to the map. Defaults to True.
        api_key (str, optional): The Google Maps API key. Defaults to None.
        **kwargs (Any): Additional arguments to pass to ipyleaflet.TileLayer().
    """

    allow_types = ["ROADMAP", "SATELLITE", "HYBRID", "TERRAIN"]
    if map_type not in allow_types:
        print("map_type must be one of the following: {}".format(allow_types))
        return

    if api_key is None:
        api_key = os.environ.get("GOOGLE_MAPS_API_KEY", None)

    if api_key is None:
        MAP_TILES = {
            "ROADMAP": {
                "url": "https://server.arcgisonline.com/ArcGIS/rest/services/World_Street_Map/MapServer/tile/{z}/{y}/{x}",
                "attribution": "Esri",
                "name": "Esri.WorldStreetMap",
            },
            "SATELLITE": {
                "url": "https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}",
                "attribution": "Esri",
                "name": "Esri.WorldImagery",
            },
            "TERRAIN": {
                "url": "https://server.arcgisonline.com/ArcGIS/rest/services/World_Topo_Map/MapServer/tile/{z}/{y}/{x}",
                "attribution": "Esri",
                "name": "Esri.WorldTopoMap",
            },
            "HYBRID": {
                "url": "https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}",
                "attribution": "Esri",
                "name": "Esri.WorldImagery",
            },
        }

    else:
        MAP_TILES = {
            "ROADMAP": GoogleMapsTileProvider(map_type="roadmap"),
            "SATELLITE": GoogleMapsTileProvider(map_type="satellite"),
            "TERRAIN": GoogleMapsTileProvider(map_type="terrain"),
            "HYBRID": GoogleMapsTileProvider(map_type="hybrid"),
        }

    if "max_zoom" not in kwargs:
        kwargs["max_zoom"] = 24

    url = MAP_TILES[map_type]["url"]

    if backend == "ipyleaflet":
        import ipyleaflet

        layer = ipyleaflet.TileLayer(
            url=url,
            name=MAP_TILES[map_type]["name"],
            attribution=MAP_TILES[map_type]["attribution"],
            visible=show,
            **kwargs,
        )
    elif backend == "folium":
        import folium

        layer = folium.TileLayer(
            tiles=url,
            name=MAP_TILES[map_type]["name"],
            attr=MAP_TILES[map_type]["attribution"],
            overlay=True,
            control=True,
            show=show,
            **kwargs,
        )
    else:
        raise ValueError("backend must be either 'ipyleaflet' or 'folium'")

    return layer


def install_package(package):
    """Install a Python package.

    Args:
        package (str | list): The package name or a GitHub URL or a list of package names or GitHub URLs.
    """
    import subprocess

    if isinstance(package, str):
        packages = [package]
    elif isinstance(package, list):
        packages = package

    for package in packages:
        if package.startswith("https"):
            package = f"git+{package}"

        # Execute pip install command and show output in real-time
        command = f"pip install {package}"
        process = subprocess.Popen(command.split(), stdout=subprocess.PIPE)

        # Print output in real-time
        while True:
            output = process.stdout.readline()
            if output == b"" and process.poll() is not None:
                break
            if output:
                print(output.decode("utf-8").strip())

        # Wait for process to complete
        process.wait()


def is_array(x):
    """Test whether x is either a numpy.ndarray or xarray.DataArray"""
    import sys

    if isinstance(x, sys.modules["numpy"].ndarray):
        return True
    if "xarray" in sys.modules:
        if isinstance(x, sys.modules["xarray"].DataArray):
            return True
    return False


def array_to_memory_file(
    array,
    source: str = None,
    dtype: str = None,
    compress: str = "deflate",
    transpose: bool = True,
    cellsize: float = None,
    crs: str = None,
    transform: tuple = None,
    driver="COG",
    colormap: dict = None,
    **kwargs: Any,
) -> Any:
    """Convert a NumPy array to a memory file.

    Args:
        array (numpy.ndarray): The input NumPy array.
        source (str, optional): Path to the source file to extract metadata from. Defaults to None.
        dtype (str, optional): The desired data type of the array. Defaults to None.
        compress (str, optional): The compression method for the output file. Defaults to "deflate".
        transpose (bool, optional): Whether to transpose the array from (bands, rows, columns) to (rows, columns, bands). Defaults to True.
        cellsize (float, optional): The cell size of the array if source is not provided. Defaults to None.
        crs (str, optional): The coordinate reference system of the array if source is not provided. Defaults to None.
        transform (tuple, optional): The affine transformation matrix if source is not provided.
            Can be rio.transform() or a tuple like (0.5, 0.0, -180.25, 0.0, -0.5, 83.780361). Defaults to None.
        driver (str, optional): The driver to use for creating the output file, such as 'GTiff'. Defaults to "COG".
        colormap (dict, optional): A dictionary defining the colormap (value: (R, G, B, A)).
        **kwargs (Any): Additional keyword arguments to be passed to the rasterio.open() function.

    Returns:
        The rasterio dataset reader object for the converted array.
    """
    import numpy as np
    import rasterio
    import xarray as xr
    from rasterio.transform import Affine

    if isinstance(array, xr.DataArray):
        coords = [coord for coord in array.coords]
        if coords[0] == "time":
            x_dim = coords[1]
            y_dim = coords[2]
            array = (
                array.isel(time=0).rename({y_dim: "y", x_dim: "x"}).transpose("y", "x")
            )
        if hasattr(array, "rio"):
            if hasattr(array.rio, "crs"):
                if array.rio.crs is not None:
                    crs = array.rio.crs
            if transform is None and hasattr(array.rio, "transform"):
                transform = array.rio.transform()
        elif source is None:
            if hasattr(array, "encoding"):
                if "source" in array.encoding:
                    source = array.encoding["source"]
        array = array.values

    if array.ndim == 3 and transpose:
        array = np.transpose(array, (1, 2, 0))
    if source is not None:
        with rasterio.open(source) as src:
            crs = src.crs
            transform = src.transform
            if compress is None:
                compress = src.compression
    else:
        if crs is None:
            raise ValueError(
                "crs must be provided if source is not provided, such as EPSG:3857"
            )

        if transform is None:
            if cellsize is None:
                raise ValueError("cellsize must be provided if source is not provided")
            # Define the geotransformation parameters
            xmin, ymin, xmax, ymax = (
                0,
                0,
                cellsize * array.shape[1],
                cellsize * array.shape[0],
            )
            # (west, south, east, north, width, height)
            transform = rasterio.transform.from_bounds(
                xmin, ymin, xmax, ymax, array.shape[1], array.shape[0]
            )
        elif isinstance(transform, Affine):
            pass
        elif isinstance(transform, (tuple, list)):
            transform = Affine(*transform)

        kwargs["transform"] = transform

    if dtype is None:
        # Determine the minimum and maximum values in the array
        min_value = np.min(array)
        max_value = np.max(array)
        # Determine the best dtype for the array
        if min_value >= 0 and max_value <= 1:
            dtype = np.float32
        elif min_value >= 0 and max_value <= 255:
            dtype = np.uint8
        elif min_value >= -128 and max_value <= 127:
            dtype = np.int8
        elif min_value >= 0 and max_value <= 65535:
            dtype = np.uint16
        elif min_value >= -32768 and max_value <= 32767:
            dtype = np.int16
        else:
            dtype = np.float64

    # Convert the array to the best dtype
    array = array.astype(dtype)
    # Define the GeoTIFF metadata
    metadata = {
        "driver": driver,
        "height": array.shape[0],
        "width": array.shape[1],
        "dtype": array.dtype,
        "crs": crs,
        "transform": transform,
    }

    if array.ndim == 2:
        metadata["count"] = 1
    elif array.ndim == 3:
        metadata["count"] = array.shape[2]
    if compress is not None:
        metadata["compress"] = compress

    metadata.update(**kwargs)

    # Create a new memory file and write the array to it
    memory_file = rasterio.MemoryFile()
    dst = memory_file.open(**metadata)

    # Check and sanitize colormap
    fixed_colormap = {}

    if colormap is None:
        colormap = {}

    for k, v in colormap.items():
        if not isinstance(k, int):
            k = int(k)
        if len(v) == 3:  # RGB
            fixed_colormap[k] = tuple(int(c) for c in v)
        elif len(v) == 4:  # RGBA
            fixed_colormap[k] = tuple(
                int(c) for c in v[:3]
            )  # Drop alpha for compatibility
        else:
            raise ValueError(f"Invalid colormap value: {v}")

    if array.ndim == 2:
        dst.write(array, 1)
        if colormap:
            dst.write_colormap(1, fixed_colormap)
    elif array.ndim == 3:
        for i in range(array.shape[2]):
            dst.write(array[:, :, i], i + 1)
            if colormap:
                dst.write_colormap(i + 1, fixed_colormap)

    dst.close()
    # Read the dataset from memory
    dataset_reader = rasterio.open(dst.name, mode="r")

    return dataset_reader


def array_to_image(
    array,
    output: str = None,
    source: str = None,
    dtype: str = None,
    compress: str = "deflate",
    transpose: bool = True,
    cellsize: float = None,
    crs: str = None,
    transform: tuple = None,
    driver: str = "COG",
    colormap: dict = None,
    **kwargs: Any,
) -> str:
    """Save a NumPy array as a GeoTIFF using the projection information from an existing GeoTIFF file.

    Args:
        array (np.ndarray): The NumPy array to be saved as a GeoTIFF.
        output (str): The path to the output image. If None, a temporary file will be created. Defaults to None.
        source (str, optional): The path to an existing GeoTIFF file with map projection information. Defaults to None.
        dtype (np.dtype, optional): The data type of the output array. Defaults to None.
        compress (str, optional): The compression method. Can be one of the following: "deflate", "lzw", "packbits", "jpeg". Defaults to "deflate".
        transpose (bool, optional): Whether to transpose the array from (bands, rows, columns) to (rows, columns, bands). Defaults to True.
        cellsize (float, optional): The resolution of the output image in meters. Defaults to None.
        crs (str, optional): The CRS of the output image. Defaults to None.
        transform (tuple, optional): The affine transformation matrix, can be rio.transform() or a tuple like (0.5, 0.0, -180.25, 0.0, -0.5, 83.780361).
            Defaults to None.
        driver (str, optional): The driver to use for creating the output file, such as 'GTiff'. Defaults to "COG".
        colormap (dict, optional): A dictionary defining the colormap (value: (R, G, B, A)).
        **kwargs (Any): Additional keyword arguments to be passed to the rasterio.open() function.
    """

    import numpy as np
    import rasterio
    import rioxarray
    import xarray as xr
    from rasterio.transform import Affine

    if output is None:
        return array_to_memory_file(
            array,
            source,
            dtype,
            compress,
            transpose,
            cellsize,
            crs=crs,
            transform=transform,
            driver=driver,
            colormap=colormap,
            **kwargs,
        )

    if isinstance(array, xr.DataArray):
        if (
            hasattr(array, "rio")
            and (array.rio.crs is not None)
            and (array.rio.transform() is not None)
        ):

            if "latitude" in array.dims and "longitude" in array.dims:
                array = array.rename({"latitude": "y", "longitude": "x"})
            elif "lat" in array.dims and "lon" in array.dims:
                array = array.rename({"lat": "y", "lon": "x"})

            if array.ndim == 2 and ("x" in array.dims) and ("y" in array.dims):
                array = array.transpose("y", "x")
            elif array.ndim == 3 and ("x" in array.dims) and ("y" in array.dims):
                dims = list(array.dims)
                dims.remove("x")
                dims.remove("y")
                array = array.transpose(dims[0], "y", "x")
            if "long_name" in array.attrs:
                array.attrs.pop("long_name")

            array.rio.to_raster(
                output, driver=driver, compress=compress, dtype=dtype, **kwargs
            )
            if colormap:
                write_image_colormap(output, colormap, output)
            return output

    if array.ndim == 3 and transpose:
        array = np.transpose(array, (1, 2, 0))

    out_dir = os.path.dirname(os.path.abspath(output))
    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    ext = os.path.splitext(output)[-1].lower()
    if ext == "":
        output += ".tif"
        driver = "COG"
    elif ext == ".png":
        driver = "PNG"
    elif ext == ".jpg" or ext == ".jpeg":
        driver = "JPEG"
    elif ext == ".jp2":
        driver = "JP2OpenJPEG"
    elif ext == ".tiff":
        driver = "GTiff"
    else:
        driver = "COG"

    if source is not None:
        with rasterio.open(source) as src:
            crs = src.crs
            transform = src.transform
            if compress is None:
                compress = src.compression
    else:
        if cellsize is None:
            raise ValueError("resolution must be provided if source is not provided")
        if crs is None:
            raise ValueError(
                "crs must be provided if source is not provided, such as EPSG:3857"
            )

        if transform is None:
            # Define the geotransformation parameters
            xmin, ymin, xmax, ymax = (
                0,
                0,
                cellsize * array.shape[1],
                cellsize * array.shape[0],
            )
            transform = rasterio.transform.from_bounds(
                xmin, ymin, xmax, ymax, array.shape[1], array.shape[0]
            )
        elif isinstance(transform, Affine):
            pass
        elif isinstance(transform, (tuple, list)):
            transform = Affine(*transform)

        kwargs["transform"] = transform

    if dtype is None:
        # Determine the minimum and maximum values in the array
        min_value = np.min(array)
        max_value = np.max(array)
        # Determine the best dtype for the array
        if min_value >= 0 and max_value <= 1:
            dtype = np.float32
        elif min_value >= 0 and max_value <= 255:
            dtype = np.uint8
        elif min_value >= -128 and max_value <= 127:
            dtype = np.int8
        elif min_value >= 0 and max_value <= 65535:
            dtype = np.uint16
        elif min_value >= -32768 and max_value <= 32767:
            dtype = np.int16
        else:
            dtype = np.float64

    # Convert the array to the best dtype
    array = array.astype(dtype)

    # Define the GeoTIFF metadata
    metadata = {
        "driver": driver,
        "height": array.shape[0],
        "width": array.shape[1],
        "dtype": array.dtype,
        "crs": crs,
        "transform": transform,
    }

    if array.ndim == 2:
        metadata["count"] = 1
    elif array.ndim == 3:
        metadata["count"] = array.shape[2]
    if compress is not None and (driver in ["GTiff", "COG"]):
        metadata["compress"] = compress

    metadata.update(**kwargs)
    # Create a new GeoTIFF file and write the array to it
    with rasterio.open(output, "w", **metadata) as dst:
        if array.ndim == 2:
            dst.write(array, 1)
            if colormap:
                dst.write_colormap(1, colormap)
        elif array.ndim == 3:
            for i in range(array.shape[2]):
                dst.write(array[:, :, i], i + 1)
                if colormap:
                    dst.write_colormap(i + 1, colormap)
    return output


def images_to_tiles(
    images: Union[str, List[str]],
    names: List[str] = None,
    ipyleaflet: bool = True,
    **kwargs: Any,
) -> Dict[str, ipyleaflet.TileLayer]:
    """Convert a list of images to a dictionary of ipyleaflet.TileLayer objects.

    Args:
        images (str | list): The path to a directory of images or a list of image paths.
        names (list, optional): A list of names for the layers. Defaults to None.
        ipyleaflet (bool, optional): Whether to return ipyleaflet.TileLayer objects. Defaults to True.
        **kwargs: Additional arguments to pass to get_local_tile_layer().

    Returns:
        A dictionary of ipyleaflet.TileLayer objects.
    """

    tiles = {}

    if isinstance(images, str):
        images = os.path.abspath(images)
        images = find_files(images, ext=".tif", recursive=False)

    if not isinstance(images, list):
        raise ValueError("images must be a list of image paths or a directory")

    if names is None:
        names = [os.path.splitext(os.path.basename(image))[0] for image in images]

    if len(names) != len(images):
        raise ValueError("names must have the same length as images")

    for index, image in enumerate(images):
        name = names[index]
        try:
            if image.startswith("http") and image.endswith(".tif"):
                url = cog_tile(image, **kwargs)
                if ipyleaflet:
                    tile = ipyleaflet.TileLayer(url=url, name=name, **kwargs)
                else:
                    tile = url
            elif image.startswith("http"):
                url = stac_tile(image, **kwargs)
                if ipyleaflet:
                    tile = ipyleaflet.TileLayer(url=url, name=name, **kwargs)
                else:
                    tile = url
            else:
                if ipyleaflet:
                    tile = get_local_tile_layer(image, layer_name=name, **kwargs)
                else:
                    tile = get_local_tile_url(image, **kwargs)
            tiles[name] = tile
        except Exception as e:
            print(image, e)

    return tiles


def get_solar_data(
    lat: float,
    lon: float,
    radiusMeters: int = 50,
    view: str = "FULL_LAYERS",
    requiredQuality: str = "HIGH",
    pixelSizeMeters: float = 0.1,
    api_key: Optional[str] = None,
    header: Optional[Dict[str, str]] = None,
    out_dir: Optional[str] = None,
    basename: Optional[str] = None,
    quiet: bool = False,
    **kwargs: Any,
) -> Dict[str, str]:
    """
    Retrieve solar data for a specific location from Google's Solar API https://developers.google.com/maps/documentation/solar.
    You need to enable Solar API from https://console.cloud.google.com/google/maps-apis/api-list.

    Args:
        lat (float): Latitude of the location.
        lon (float): Longitude of the location.
        radiusMeters (int, optional): Radius in meters for the data retrieval (default is 50).
        view (str, optional): View type (default is "FULL_LAYERS"). For more options, see https://bit.ly/3LazuBi.
        requiredQuality (str, optional): Required quality level (default is "HIGH").
        pixelSizeMeters (float, optional): Pixel size in meters (default is 0.1).
        api_key (str, optional): Google API key for authentication (if not provided, checks 'GOOGLE_API_KEY' environment variable).
        header (dict, optional): Additional HTTP headers to include in the request.
        out_dir (str, optional): Directory where downloaded files will be saved.
        basename (str, optional): Base name for the downloaded files (default is generated from imagery date).
        quiet (bool, optional): If True, suppress progress messages during file downloads (default is False).
        **kwargs: Additional keyword arguments to be passed to the download_file function.

    Returns:
        Dict[str, str]: A dictionary mapping file names to their corresponding paths.
    """

    if api_key is None:
        api_key = os.environ.get("GOOGLE_API_KEY", "")

    if api_key == "":
        raise ValueError("GOOGLE_API_KEY is required to use this function.")

    url = "https://solar.googleapis.com/v1/dataLayers:get"
    params = {
        "location.latitude": lat,
        "location.longitude": lon,
        "radiusMeters": radiusMeters,
        "view": view,
        "requiredQuality": requiredQuality,
        "pixelSizeMeters": pixelSizeMeters,
        "key": api_key,
    }

    solar_data = requests.get(url, params=params, headers=header).json()

    links = {}

    for key in solar_data.keys():
        if "Url" in key:
            if isinstance(solar_data[key], list):
                urls = [url + "&key=" + api_key for url in solar_data[key]]
                links[key] = urls
            else:
                links[key] = solar_data[key] + "&key=" + api_key

    if basename is None:
        date = solar_data["imageryDate"]
        year = date["year"]
        month = date["month"]
        day = date["day"]
        basename = f"{year}_{str(month).zfill(2)}_{str(day).zfill(2)}"

    filenames = {}

    for link in links:
        if isinstance(links[link], list):
            for i, url in enumerate(links[link]):
                filename = (
                    f"{basename}_{link.replace('Urls', '')}_{str(i+1).zfill(2)}.tif"
                )
                if out_dir is not None:
                    filename = os.path.join(out_dir, filename)
                download_file(url, filename, quiet=quiet, **kwargs)
                filenames[link.replace("Urls", "") + "_" + str(i).zfill(2)] = filename
        else:
            name = link.replace("Url", "")
            filename = f"{basename}_{name}.tif"
            if out_dir is not None:
                filename = os.path.join(out_dir, filename)
            download_file(links[link], filename, quiet=quiet, **kwargs)
            filenames[name] = filename

    return filenames


def merge_vector(
    files: Union[str, List[str]],
    output: str = None,
    crs: str = None,
    ext: str = "geojson",
    recursive: bool = False,
    quiet: bool = False,
    return_gdf: bool = False,
    **kwargs: Any,
) -> Optional["gpd.GeoDataFrame"]:
    """
    Merge vector files into a single GeoDataFrame.

    Args:
        files: A string or a list of file paths to be merged.
        output: The file path to save the merged GeoDataFrame.
        crs: Optional. The coordinate reference system (CRS) of the output GeoDataFrame.
        ext: Optional. The file extension of the input files. Default is 'geojson'.
        recursive: Optional. If True, search for files recursively in subdirectories. Default is False.
        quiet: Optional. If True, suppresses progress messages. Default is False.
        return_gdf: Optional. If True, returns the merged GeoDataFrame. Default is False.
        **kwargs (Any): Additional keyword arguments to be passed to the `gpd.read_file` function.

    Returns:
        If `return_gdf` is True, returns the merged GeoDataFrame. Otherwise, returns None.

    Raises:
        TypeError: If `files` is not a list of file paths.

    """

    import geopandas as gpd
    import pandas as pd

    if isinstance(files, str):
        files = find_files(files, ext=ext, recursive=recursive)

    if not isinstance(files, list):
        raise TypeError("files must be a list of file paths")

    gdfs = []
    for index, filename in enumerate(files):
        if not quiet:
            print(f"Reading {index+1} of {len(files)}: {filename}")
        gdf = gpd.read_file(filename, **kwargs)
        if crs is None:
            crs = gdf.crs
        else:
            gdf = gdf.to_crs(crs)
        gdfs.append(gdf)

    if not quiet:
        print("Merging GeoDataFrames ...")
    gdf = gpd.GeoDataFrame(pd.concat(gdfs, ignore_index=True), crs=crs)

    if output is not None:
        if not quiet:
            print(f"Saving merged file to {output} ...")
        gdf.to_file(output)
        print(f"Saved merged file to {output}")

    if return_gdf:
        return gdf


def download_ms_buildings(
    location: str,
    out_dir: Optional[str] = None,
    merge_output: Optional[str] = None,
    head=None,
    quiet: bool = False,
    **kwargs: Any,
) -> List[str]:
    """
    Download Microsoft Buildings dataset for a specific location. Check the dataset links from
        https://minedbuildings.blob.core.windows.net/global-buildings/dataset-links.csv.

    Args:
        location (str): The location name for which to download the dataset.
        out_dir (str, optional): The output directory to save the downloaded files. If not provided, the current working directory is used.
        merge_output (str, optional): The output file path for merging the downloaded files into a single GeoDataFrame.
        head (int, optional): The number of files to download. If not provided, all files will be downloaded.
        quiet (bool, optional): If True, suppresses the download progress messages.
        **kwargs (Any): Additional keyword arguments to be passed to the `gpd.to_file` function.

    Returns:
        A list of file paths of the downloaded files.

    """

    import geopandas as gpd
    import pandas as pd
    from shapely.geometry import shape

    if out_dir is None:
        out_dir = os.getcwd()

    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    dataset_links = pd.read_csv(
        "https://minedbuildings.blob.core.windows.net/global-buildings/dataset-links.csv"
    )
    country_links = dataset_links[dataset_links.Location == location]

    if not quiet:
        print(f"Found {len(country_links)} links for {location}")
    if head is not None:
        country_links = country_links.head(head)

    filenames = []
    i = 1

    for _, row in country_links.iterrows():
        if not quiet:
            print(f"Downloading {i} of {len(country_links)}: {row.QuadKey}.geojson")
        i += 1
        filename = os.path.join(out_dir, f"{row.QuadKey}.geojson")
        filenames.append(filename)
        if os.path.exists(filename):
            print(f"File {filename} already exists, skipping...")
            continue
        df = pd.read_json(row.Url, lines=True)
        df["geometry"] = df["geometry"].apply(shape)
        gdf = gpd.GeoDataFrame(df, crs=4326)
        gdf.to_file(filename, driver="GeoJSON", **kwargs)

    if merge_output is not None:
        if os.path.exists(merge_output):
            print(f"File {merge_output} already exists, skip merging...")
            return filenames
        merge_vector(filenames, merge_output, quiet=quiet)

    return filenames


def download_google_buildings(
    location: str,
    out_dir: Optional[str] = None,
    merge_output: Optional[str] = None,
    head: Optional[int] = None,
    keep_geojson: bool = False,
    overwrite: bool = False,
    quiet: bool = False,
    **kwargs: Any,
) -> List[str]:
    """
    Download Google Open Building dataset for a specific location. Check the dataset links from
        https://sites.research.google/open-buildings.

    Args:
        location: The location name for which to download the dataset.
        out_dir: The output directory to save the downloaded files. If not provided, the current working directory is used.
        merge_output: Optional. The output file path for merging the downloaded files into a single GeoDataFrame.
        head: Optional. The number of files to download. If not provided, all files will be downloaded.
        keep_geojson: Optional. If True, the GeoJSON files will be kept after converting them to CSV files.
        overwrite: Optional. If True, overwrite the existing files.
        quiet: Optional. If True, suppresses the download progress messages.
        **kwargs: Additional keyword arguments to be passed to the `gpd.to_file` function.

    Returns:
        A list of file paths of the downloaded files.

    """

    import geopandas as gpd
    import pandas as pd
    from shapely import wkt

    building_url = "https://sites.research.google/open-buildings/tiles.geojson"
    country_url = (
        "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip"
    )

    if out_dir is None:
        out_dir = os.getcwd()

    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    building_gdf = gpd.read_file(building_url)
    country_gdf = gpd.read_file(country_url)

    country = country_gdf[country_gdf["NAME"] == location]

    if len(country) == 0:
        country = country_gdf[country_gdf["NAME_LONG"] == location]
        if len(country) == 0:
            raise ValueError(f"Could not find {location} in the Natural Earth dataset.")

    gdf = building_gdf[building_gdf.intersects(country.geometry.iloc[0])]
    gdf.sort_values(by="size_mb", inplace=True)

    print(f"Found {len(gdf)} links for {location}.")
    if head is not None:
        gdf = gdf.head(head)

    if len(gdf) > 0:
        links = gdf["tile_url"].tolist()
        download_files(links, out_dir=out_dir, quiet=quiet, **kwargs)
        filenames = [os.path.join(out_dir, os.path.basename(link)) for link in links]

        gdfs = []
        for filename in filenames:
            # Read the CSV file into a pandas DataFrame
            df = pd.read_csv(filename)

            # Create a geometry column from the "geometry" column in the DataFrame
            df["geometry"] = df["geometry"].apply(wkt.loads)

            # Convert the pandas DataFrame to a GeoDataFrame
            gdf = gpd.GeoDataFrame(df, geometry="geometry")
            gdf.crs = "EPSG:4326"
            if keep_geojson:
                gdf.to_file(
                    filename.replace(".csv.gz", ".geojson"), driver="GeoJSON", **kwargs
                )
            gdfs.append(gdf)

        if merge_output:
            if os.path.exists(merge_output) and not overwrite:
                print(f"File {merge_output} already exists, skip merging...")
            else:
                if not quiet:
                    print("Merging GeoDataFrames ...")
                gdf = gpd.GeoDataFrame(
                    pd.concat(gdfs, ignore_index=True), crs="EPSG:4326"
                )
                gdf.to_file(merge_output, **kwargs)

    else:
        print(f"No buildings found for {location}.")


def google_buildings_csv_to_vector(
    filename: str, output: Optional[str] = None, **kwargs
) -> None:
    """
    Convert a CSV file containing Google Buildings data to a GeoJSON vector file.

    Args:
        filename (str): The path to the input CSV file.
        output (str, optional): The path to the output GeoJSON file. If not provided, the output file will have the same
            name as the input file with the extension changed to '.geojson'.
        **kwargs: Additional keyword arguments that are passed to the `to_file` method of the GeoDataFrame.

    Returns:
        None
    """
    import geopandas as gpd
    import pandas as pd
    from shapely import wkt

    df = pd.read_csv(filename)

    # Create a geometry column from the "geometry" column in the DataFrame
    df["geometry"] = df["geometry"].apply(wkt.loads)

    # Convert the pandas DataFrame to a GeoDataFrame
    gdf = gpd.GeoDataFrame(df, geometry="geometry")
    gdf.crs = "EPSG:4326"

    if output is None:
        output = os.path.splitext(filename)[0] + ".geojson"

    gdf.to_file(output, **kwargs)


def widget_template(
    widget=None,
    opened=True,
    show_close_button=True,
    widget_icon="gear",
    close_button_icon="times",
    widget_args={},
    close_button_args={},
    display_widget=None,
    m=None,
    position="topright",
):
    """Create a widget template.

    Args:
        widget (ipywidgets.Widget, optional): The widget to be displayed. Defaults to None.
        opened (bool, optional): Whether to open the toolbar. Defaults to True.
        show_close_button (bool, optional): Whether to show the close button. Defaults to True.
        widget_icon (str, optional): The icon name for the toolbar button. Defaults to 'gear'.
        close_button_icon (str, optional): The icon name for the close button. Defaults to "times".
        widget_args (dict, optional): Additional arguments to pass to the toolbar button. Defaults to {}.
        close_button_args (dict, optional): Additional arguments to pass to the close button. Defaults to {}.
        display_widget (ipywidgets.Widget, optional): The widget to be displayed when the toolbar is clicked.
        m (geemap.Map, optional): The geemap.Map instance. Defaults to None.
        position (str, optional): The position of the toolbar. Defaults to "topright".
    """

    name = "_" + random_string()  # a random attribute name

    if "value" not in widget_args:
        widget_args["value"] = False
    if "tooltip" not in widget_args:
        widget_args["tooltip"] = "Toolbar"
    if "layout" not in widget_args:
        widget_args["layout"] = widgets.Layout(
            width="28px", height="28px", padding="0px 0px 0px 4px"
        )
    widget_args["icon"] = widget_icon

    if "value" not in close_button_args:
        close_button_args["value"] = False
    if "tooltip" not in close_button_args:
        close_button_args["tooltip"] = "Close the tool"
    if "button_style" not in close_button_args:
        close_button_args["button_style"] = "primary"
    if "layout" not in close_button_args:
        close_button_args["layout"] = widgets.Layout(
            height="28px", width="28px", padding="0px 0px 0px 4px"
        )
    close_button_args["icon"] = close_button_icon

    toolbar_button = widgets.ToggleButton(**widget_args)

    close_button = widgets.ToggleButton(**close_button_args)

    toolbar_widget = widgets.VBox()
    toolbar_widget.children = [toolbar_button]
    toolbar_header = widgets.HBox()
    if show_close_button:
        toolbar_header.children = [close_button, toolbar_button]
    else:
        toolbar_header.children = [toolbar_button]
    toolbar_footer = widgets.VBox()

    if widget is not None:
        toolbar_footer.children = [
            widget,
        ]
    else:
        toolbar_footer.children = []

    def toolbar_btn_click(change):
        if change["new"]:
            close_button.value = False
            toolbar_widget.children = [toolbar_header, toolbar_footer]
            if display_widget is not None:
                widget.clear_output()
                widget.outputs = ()
                with widget:
                    widget.append_display_date(display_widget)
        else:
            toolbar_widget.children = [toolbar_button]

    toolbar_button.observe(toolbar_btn_click, "value")

    def close_btn_click(change):
        if change["new"]:
            toolbar_button.value = False
            if m is not None:
                control = getattr(m, name)
                if control is not None and control in m.controls:
                    m.remove_control(control)
                    delattr(m, name)
            toolbar_widget.close()

    close_button.observe(close_btn_click, "value")

    toolbar_button.value = opened
    if m is not None:
        import ipyleaflet

        toolbar_control = ipyleaflet.WidgetControl(
            widget=toolbar_widget, position=position
        )

        if toolbar_control not in m.controls:
            m.add(toolbar_control)

            setattr(m, name, toolbar_control)

    else:
        return toolbar_widget


def start_server(
    directory: str = None, port: int = 8000, background: bool = True, quiet: bool = True
) -> None:
    """
    Start a simple web server to serve files from the specified directory
    with directory listing and CORS support. Optionally, run the server
    asynchronously in a background thread.

    Args:
        directory (str): The directory from which files will be served.
        port (int, optional): The port on which the web server will run. Defaults to 8000.
        background (bool, optional): Whether to run the server in a separate background thread.
                                     Defaults to True.
        quiet (bool, optional): If True, suppress the log output. Defaults to True.

    Raises:
        ImportError: If required modules are not found.
        Exception: Catches other unexpected errors during execution.

    Returns:
        None. The function runs the server indefinitely until manually stopped.
    """

    # If no directory is specified, use the current working directory
    if directory is None:
        directory = os.getcwd()

    def run_flask():
        try:
            from flask import Flask, render_template_string, send_from_directory
            from flask_cors import CORS

            app = Flask(__name__, static_folder=directory)
            CORS(app)  # Enable CORS for all routes

            if quiet:
                # This will disable Flask's logging
                import logging

                log = logging.getLogger("werkzeug")
                log.disabled = True
                app.logger.disabled = True

            @app.route("/<path:path>", methods=["GET"])
            def serve_file(path):
                return send_from_directory(directory, path)

            @app.route("/", methods=["GET"])
            def index():
                # List files and directories under the specified directory
                items = os.listdir(directory)
                items.sort()
                # Generate an HTML representation of the directory listing
                listing_template = """
                <h2>Directory listing for /</h2>
                <hr>
                <ul>
                    {% for item in items %}
                        <li><a href="{{ item }}">{{ item }}</a></li>
                    {% endfor %}
                </ul>
                """
                return render_template_string(listing_template, items=items)

            print(f"Server is running at http://127.0.0.1:{port}/")
            app.run(port=port)

        except ImportError as e:
            print(f"Error importing module: {e}")
        except Exception as e:
            print(f"An error occurred: {e}")

    if background:
        import threading

        # Start the Flask server in a new background thread
        t = threading.Thread(target=run_flask)
        t.start()
    else:
        # Run the Flask server in the main thread
        run_flask()


def init_duckdb_tiles(
    data,
    database_path: str = ":memory:",
    table_name: str = "features",
    geom_column: str = "geom",
    srid: int = 3857,
    quiet: bool = False,
    use_view: bool = False,
    src_crs: str = None,
) -> str:
    """
    Initialize a DuckDB database with spatial data for vector tile serving.

    This function creates a DuckDB database and loads spatial data from various
    sources into a table suitable for serving vector tiles. The geometry is
    automatically transformed to Web Mercator (EPSG:3857) for tile generation.

    Supports all vector formats that DuckDB's ST_Read can handle, including:
    - GeoJSON (.geojson, .json)
    - Shapefile (.shp)
    - GeoPackage (.gpkg)
    - FlatGeobuf (.fgb)
    - GeoParquet (.parquet, .geoparquet)
    - And many more GDAL-supported formats

    Args:
        data: The spatial data to load. Can be:
            - Path to a vector file (str) - Any format supported by DuckDB's ST_Read
            - GeoJSON dictionary
            - GeoDataFrame
        database_path (str, optional): Path to the DuckDB database file.
            Use ":memory:" for an in-memory database. Defaults to ":memory:".
        table_name (str, optional): Name of the table to create in the database.
            Defaults to "features".
        geom_column (str, optional): Name of the geometry column. Defaults to "geom".
        srid (int, optional): Target SRID for the geometry. Defaults to 3857 (Web Mercator).
        quiet (bool, optional): If True, suppress progress messages. Defaults to False.
        use_view (bool, optional): If True and data is a parquet file, create a view instead
            of a table. Views avoid data duplication but may be slower for tile serving as they
            query the source file on each request. Only applies to parquet files. Defaults to False.
        src_crs (str, optional): Source CRS of the input data as an EPSG code (e.g., 'EPSG:5070',
            'EPSG:4326'). If None, will attempt to auto-detect. Specify this parameter if the data
            is in a projected CRS that is not Web Mercator. Defaults to None.

    Returns:
        str: The path to the created database.

    Raises:
        ImportError: If duckdb is not installed.
        Exception: If data loading or transformation fails.

    Example:
        >>> import leafmap
        >>> # From GeoJSON file
        >>> db_path = leafmap.init_duckdb_tiles(
        ...     "data.geojson",
        ...     database_path="tiles.db",
        ...     table_name="buildings"
        ... )
        >>> # From Shapefile
        >>> db_path = leafmap.init_duckdb_tiles("data.shp", database_path="tiles.db")
        >>> # From GeoPackage
        >>> db_path = leafmap.init_duckdb_tiles("data.gpkg", database_path="tiles.db")
        >>> # From GeoDataFrame
        >>> import geopandas as gpd
        >>> gdf = gpd.read_file("data.geojson")
        >>> db_path = leafmap.init_duckdb_tiles(gdf, database_path="tiles.db")
    """
    try:
        import duckdb
    except ImportError:
        raise ImportError(
            "duckdb is required for this function. "
            "Install it with: pip install duckdb"
        )

    # Handle different input types
    if isinstance(data, str):
        # File path - ST_Read will handle any format DuckDB supports
        # (GeoJSON, Shapefile, GeoPackage, FlatGeobuf, GeoParquet, etc.)
        input_path = data
        is_file = True
        # Check if it's a Parquet file
        is_parquet = input_path.lower().endswith((".parquet", ".geoparquet"))
    else:
        is_parquet = False
        # GeoDataFrame or GeoJSON dict - convert to temporary GeoJSON file
        # Note: This only applies to Python objects, not file paths
        try:
            import geopandas as gpd

            if isinstance(data, dict):
                gdf = gpd.GeoDataFrame.from_features(data)
            elif isinstance(data, gpd.GeoDataFrame):
                gdf = data
            else:
                raise ValueError(
                    "Data must be a file path, GeoJSON dict, or GeoDataFrame"
                )

            # Save to temporary GeoJSON file for ST_Read to consume
            import tempfile

            temp_file = tempfile.NamedTemporaryFile(
                delete=False, suffix=".geojson", mode="w"
            )
            gdf.to_file(temp_file.name, driver="GeoJSON")
            input_path = temp_file.name
            is_file = False
        except ImportError:
            raise ImportError(
                "geopandas is required for GeoDataFrame input. "
                "Install it with: pip install geopandas"
            )

    # Create or connect to database
    con = duckdb.connect(database_path, read_only=False)

    try:
        # Install and load spatial extension
        con.execute("INSTALL spatial;")
        con.execute("LOAD spatial;")

        # Handle Parquet files differently - use DuckDB's native Parquet reader
        if is_parquet:
            # For Parquet files, read directly without ST_Read
            # First check if httpfs extension is needed for remote files
            if input_path.startswith(("http://", "https://", "s3://")):
                con.execute("INSTALL httpfs;")
                con.execute("LOAD httpfs;")
                # Configure for public S3 buckets
                if input_path.startswith("s3://"):
                    con.execute("SET s3_region='us-west-2';")
                    con.execute("SET s3_url_style='path';")

            # Get column names and types from Parquet file
            temp_result = con.execute(f"SELECT * FROM '{input_path}' LIMIT 0")
            all_columns = [(desc[0], str(desc[1])) for desc in temp_result.description]

            # Find geometry column (common names: geometry, geom, wkb_geometry, etc.)
            geom_col_source = None
            for col_name, col_type in all_columns:
                if col_name.lower() in ["geometry", "geom", "wkb_geometry", "shape"]:
                    geom_col_source = col_name
                    break

            if not geom_col_source:
                col_names = [col[0] for col in all_columns]
                raise Exception(
                    f"No geometry column found in Parquet file. Available columns: {col_names}"
                )

            # Exclude geometry columns and complex types (STRUCT, LIST, MAP, JSON, etc.)
            # These types are not supported by ST_AsMVT and can cause issues
            non_geom_columns = [
                col_name
                for col_name, col_type in all_columns
                if col_name.lower()
                not in [
                    "geom",
                    "geometry",
                    "wkb_geometry",
                    "shape",
                    "geom_bbox",
                    "bbox",
                ]
                and not any(
                    complex_type in col_type.upper()
                    for complex_type in ["STRUCT", "LIST", "MAP", "JSON", "ARRAY"]
                )
            ]

            # Build SELECT clause with quoted column names to handle dots in names
            if non_geom_columns:
                quoted_columns = [f'"{col}"' for col in non_geom_columns]
                select_clause = ", ".join(quoted_columns) + ", "
            else:
                select_clause = ""

            source_table = f"'{input_path}'"
        else:
            # Use ST_Read for other formats (GeoJSON, Shapefile, etc.)
            # First, get column names and types from the source to exclude geometry and complex types
            temp_result = con.execute(f"SELECT * FROM ST_Read('{input_path}') LIMIT 0")
            all_columns = [(desc[0], str(desc[1])) for desc in temp_result.description]

            # Find the geometry column (ST_Read may use 'geom' or 'geometry')
            geom_col_source = None
            for col_name, col_type in all_columns:
                if "GEOMETRY" in col_type.upper() or col_name.lower() in [
                    "geom",
                    "geometry",
                    "wkb_geometry",
                    "shape",
                ]:
                    geom_col_source = col_name
                    break

            if not geom_col_source:
                # Default to 'geom' which is what ST_Read usually uses
                geom_col_source = "geom"

            # Exclude geometry columns and complex types (STRUCT, LIST, MAP, JSON, etc.)
            non_geom_columns = [
                col_name
                for col_name, col_type in all_columns
                if col_name.lower()
                not in [
                    "geom",
                    "geometry",
                    "geom_bbox",
                    "bbox",
                    "wkb_geometry",
                    "shape",
                ]
                and "GEOMETRY" not in col_type.upper()
                and not any(
                    complex_type in col_type.upper()
                    for complex_type in ["STRUCT", "LIST", "MAP", "JSON", "ARRAY"]
                )
            ]

            # Build SELECT clause with non-geometry columns (quoted to handle dots in names)
            if non_geom_columns:
                quoted_columns = [f'"{col}"' for col in non_geom_columns]
                select_clause = ", ".join(quoted_columns) + ", "
            else:
                select_clause = ""

            source_table = f"ST_Read('{input_path}')"

        # Check if data is already in the target SRID by examining a sample geometry
        # Try to detect the current CRS - if it's already in EPSG:3857, don't transform
        # NOTE: ST_Transform is broken in DuckDB 1.4.1, always returns infinity
        # So we skip transformation and keep data in original CRS (typically EPSG:4326)
        try:
            sample_geom = con.execute(
                f"SELECT {geom_col_source} FROM {source_table} LIMIT 1"
            ).fetchone()
            if sample_geom and sample_geom[0]:
                # Determine if we need to transform based on user input or auto-detection
                needs_transform = True
                transform_from_crs = src_crs if src_crs else "EPSG:4326"

                if src_crs:
                    # User specified source CRS - always transform unless it's already Web Mercator
                    if src_crs.upper() in ["EPSG:3857", "3857"]:
                        needs_transform = False
                        if not quiet:
                            print(f"Data is already in Web Mercator ({src_crs})")
                    else:
                        needs_transform = True
                        if not quiet:
                            print(f"Transforming from {src_crs} to EPSG:3857")
                else:
                    # Auto-detect CRS based on coordinate ranges
                    coords_check = con.execute(
                        f"SELECT ST_X(ST_Centroid({geom_col_source})), ST_Y(ST_Centroid({geom_col_source})) FROM {source_table} LIMIT 1"
                    ).fetchone()
                    if coords_check and coords_check[0]:
                        x, y = coords_check[0], coords_check[1]
                        # Auto-detect based on coordinate ranges
                        # Reference: https://epsg.io/

                        if abs(x) <= 180 and abs(y) <= 90:
                            # WGS84 (EPSG:4326) - Geographic coordinates
                            # Range: X: [-180, 180], Y: [-90, 90]
                            needs_transform = True
                            transform_from_crs = "EPSG:4326"
                            if not quiet:
                                print("Auto-detected CRS: EPSG:4326 (WGS84)")
                        elif 160_000 < x < 780_000 and -300_000 < y < 3_700_000:
                            # Likely UTM zones (various EPSGs)
                            # UTM coordinates typically in range [160k-780k, 0-10M]
                            # Without zone info, can't determine exact EPSG
                            if not quiet:
                                print(
                                    f"Warning: Coordinates appear to be in UTM projection"
                                )
                                print(f"  Sample: X={x:.2f}, Y={y:.2f}")
                                print(f"  Cannot determine UTM zone automatically")
                                print(
                                    f"  Specify src_crs (e.g., 'EPSG:32618' for UTM Zone 18N)"
                                )
                            # Try as if it's already in Web Mercator (will likely fail)
                            needs_transform = False
                        elif -2_500_000 < x < 2_500_000 and -2_500_000 < y < 6_300_000:
                            # Likely EPSG:5070 (NAD83 / Conus Albers)
                            # Range: X: [-2.3M, 2.5M], Y: [0, 3.2M] for CONUS
                            # Or EPSG:2163 (US National Atlas Equal Area)
                            needs_transform = True
                            transform_from_crs = "EPSG:5070"
                            if not quiet:
                                print(
                                    f"Auto-detected CRS: EPSG:5070 (NAD83 / Conus Albers)"
                                )
                                print(f"  Sample coordinates: X={x:.2f}, Y={y:.2f}")
                                print(
                                    f"  Use src_crs='EPSG:5070' to override if incorrect"
                                )
                        elif abs(x) <= 20037508 and abs(y) <= 20037508:
                            # Web Mercator (EPSG:3857)
                            # Range: ±20,037,508 meters (max extent)
                            needs_transform = False
                            if not quiet:
                                print("Auto-detected CRS: EPSG:3857 (Web Mercator)")
                        else:
                            # Unknown projected CRS
                            if not quiet:
                                print(f"Warning: Could not auto-detect CRS")
                                print(f"  Sample coordinates: X={x:.2f}, Y={y:.2f}")
                                print(
                                    f"  Specify src_crs parameter (e.g., src_crs='EPSG:5070', 'EPSG:32618', etc.)"
                                )
                                print(
                                    f"  Data will be loaded without transformation (may appear in wrong location)"
                                )
                            # Load without transformation as fallback
                            needs_transform = False

                if not needs_transform:
                    # Data is already in Web Mercator, don't transform
                    # Use VIEW for Parquet files only if user requested it
                    table_or_view = "VIEW" if (is_parquet and use_view) else "TABLE"
                    con.execute(
                        f"""
                        CREATE {table_or_view} IF NOT EXISTS {table_name} AS
                        SELECT {select_clause}{geom_col_source} as {geom_column}
                        FROM {source_table};
                    """
                    )
                    if not quiet:
                        view_note = " (using view)" if (is_parquet and use_view) else ""
                        print(
                            f"Successfully loaded data into {table_or_view.lower()} '{table_name}' (already in Web Mercator){view_note}"
                        )
                else:
                    # Data needs transformation to Web Mercator
                    # Use VIEW for Parquet files only if user requested it
                    table_or_view = "VIEW" if (is_parquet and use_view) else "TABLE"
                    con.execute(
                        f"""
                        CREATE {table_or_view} IF NOT EXISTS {table_name} AS
                        SELECT
                            {select_clause}
                            ST_Transform(ST_GeomFromWKB(ST_AsWKB({geom_col_source})), '{transform_from_crs}', 'EPSG:{srid}', true) as {geom_column}
                        FROM {source_table};
                    """
                    )
                    if not quiet:
                        view_note = " (using view)" if (is_parquet and use_view) else ""
                        print(
                            f"Successfully loaded data into {table_or_view.lower()} '{table_name}' (transformed from {transform_from_crs} to EPSG:{srid}){view_note}"
                        )
            else:
                raise Exception("No geometry data found")
        except Exception as crs_error:
            # If CRS detection fails, try loading without transformation
            if not quiet:
                print(
                    f"CRS detection failed ({crs_error}), loading without transformation"
                )
            # Use VIEW for Parquet files only if user requested it
            table_or_view = "VIEW" if (is_parquet and use_view) else "TABLE"
            con.execute(
                f"""
                CREATE {table_or_view} IF NOT EXISTS {table_name} AS
                SELECT {select_clause}{geom_col_source} as {geom_column}
                FROM {source_table};
            """
            )
            if not quiet:
                view_note = " (using view)" if (is_parquet and use_view) else ""
                print(
                    f"Loaded data into {table_or_view.lower()} '{table_name}' (original CRS){view_note}"
                )

        con.commit()

        # Get row count
        row_count = con.execute(f"SELECT COUNT(*) FROM {table_name}").fetchone()[0]
        if not quiet:
            print(f"Total features: {row_count}")

    except Exception as e:
        if not quiet:
            print(f"Error loading data: {e}")
        # Try alternative approach without transformation
        try:
            # Determine if we need to use Parquet-specific handling
            if is_parquet:
                # For Parquet, we already have the source_table and geom_col_source from above
                # Use VIEW for Parquet files only if user requested it
                table_or_view = "VIEW" if use_view else "TABLE"
                con.execute(
                    f"""
                    CREATE {table_or_view} IF NOT EXISTS {table_name} AS
                    SELECT {select_clause}{geom_col_source} as {geom_column}
                    FROM {source_table};
                """
                )
            else:
                # First, get column names and types to exclude geometry and complex types
                temp_result = con.execute(
                    f"SELECT * FROM ST_Read('{input_path}') LIMIT 0"
                )
                all_columns = [
                    (desc[0], str(desc[1])) for desc in temp_result.description
                ]

                # Find the geometry column
                geom_col_source = None
                for col_name, col_type in all_columns:
                    if "GEOMETRY" in col_type.upper() or col_name.lower() in [
                        "geom",
                        "geometry",
                        "wkb_geometry",
                        "shape",
                    ]:
                        geom_col_source = col_name
                        break

                if not geom_col_source:
                    geom_col_source = "geom"  # Default

                non_geom_columns = [
                    col_name
                    for col_name, col_type in all_columns
                    if col_name.lower()
                    not in [
                        "geom",
                        "geometry",
                        "geom_bbox",
                        "bbox",
                        "wkb_geometry",
                        "shape",
                    ]
                    and "GEOMETRY" not in col_type.upper()
                    and not any(
                        complex_type in col_type.upper()
                        for complex_type in ["STRUCT", "LIST", "MAP", "JSON", "ARRAY"]
                    )
                ]

                if non_geom_columns:
                    quoted_columns = [f'"{col}"' for col in non_geom_columns]
                    select_clause = ", ".join(quoted_columns) + ", "
                else:
                    select_clause = ""

                con.execute(
                    f"""
                    CREATE TABLE IF NOT EXISTS {table_name} AS
                    SELECT {select_clause}"{geom_col_source}" as {geom_column}
                    FROM ST_Read('{input_path}');
                """
                )
            con.commit()
            if not quiet:
                view_or_table = "view" if (is_parquet and use_view) else "table"
                view_note = " (using view)" if (is_parquet and use_view) else ""
                print(
                    f"Loaded data without transformation into {view_or_table} '{table_name}' (original CRS){view_note}"
                )
        except Exception as e2:
            raise Exception(f"Failed to load data: {e2}")
    finally:
        con.close()
        # Clean up temporary file if created
        if not is_file:
            import os

            try:
                os.unlink(input_path)
            except Exception:
                pass

    return database_path


# Global registry to track DuckDB connection pools for each database
# Key: database_path, Value: dict with 'pool' and 'lock'
_duckdb_connection_pools = {}


def start_duckdb_tile_server(
    database_path: str,
    table_name: str = "features",
    geom_column: str = "geom",
    properties: list = None,
    port: int = 8000,
    background: bool = True,
    quiet: bool = True,
    cors: bool = True,
    min_zoom: int = None,
    src_crs: str = None,
) -> int:
    """
    Start a Flask server that serves vector tiles from a DuckDB database.

    This function creates a Flask web server with a vector tile endpoint that
    generates Mapbox Vector Tiles (MVT) on-the-fly from a DuckDB database using
    the ST_AsMVT function. The tiles can be consumed by MapLibre GL JS and other
    vector tile clients.

    If the specified port is already in use, the function will automatically
    find the next available port.

    Args:
        database_path (str): Path to the DuckDB database file.
        table_name (str, optional): Name of the table containing the spatial data.
            Defaults to "features".
        geom_column (str, optional): Name of the geometry column. Defaults to "geom".
        properties (list, optional): List of property columns to include in tiles.
            If None, includes all columns except geometry. Defaults to None.
        port (int, optional): Port number for the server. Defaults to 8000.
            If the port is in use, will automatically use the next available port.
        background (bool, optional): Whether to run the server in a background thread.
            Defaults to True.
        quiet (bool, optional): If True, suppress server logs. Defaults to True.
        cors (bool, optional): Whether to enable CORS. Defaults to True.
        min_zoom (int, optional): Minimum zoom level at which to query and serve tiles.
            Below this zoom level, empty tiles will be returned, preventing memory issues
            with large datasets. If None, tiles will be served at all zoom levels. Defaults to None.
        src_crs (str, optional): Source CRS of the data in the database as an EPSG code
            (e.g., 'EPSG:26918', 'EPSG:4326'). If provided, geometries will be transformed
            on-the-fly from this CRS to Web Mercator when serving tiles. Only needed if the
            data in the database is not already in Web Mercator (EPSG:3857). Defaults to None.

    Returns:
        int: The actual port number being used by the server.

    Raises:
        ImportError: If required modules (flask, duckdb) are not installed.

    Example:
        >>> import leafmap
        >>> # Initialize database
        >>> db_path = leafmap.init_duckdb_tiles("data.geojson", database_path="tiles.db")
        >>> # Start tile server
        >>> actual_port = leafmap.start_duckdb_tile_server(
        ...     database_path=db_path,
        ...     table_name="features",
        ...     port=8000
        ... )
        >>> print(f"Server running on port {actual_port}")
        >>> # Server is now running at http://127.0.0.1:{actual_port}/tiles/{z}/{x}/{y}.pbf
    """

    # Find an available port
    import socket

    def find_available_port(start_port: int, max_attempts: int = 100) -> int:
        """Find an available port starting from start_port."""
        for port_to_try in range(start_port, start_port + max_attempts):
            try:
                with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
                    s.bind(("127.0.0.1", port_to_try))
                    return port_to_try
            except OSError:
                continue
        raise RuntimeError(
            f"Could not find an available port in range {start_port}-{start_port + max_attempts}"
        )

    actual_port = find_available_port(port)
    if actual_port != port and not quiet:
        print(
            f"Port {port} is in use, using port {actual_port} instead for DuckDB tile server"
        )

    def run_flask():
        try:
            import duckdb
            from flask import Flask, Response

            if cors:
                from flask_cors import CORS

            app = Flask(__name__)

            if cors:
                CORS(
                    app,
                    resources={r"/*": {"origins": "*"}},
                    allow_headers=["Content-Type"],
                    methods=["GET", "HEAD", "OPTIONS"],
                )

            # Always suppress werkzeug's development server warning
            import logging
            import warnings

            # Suppress the development server warning
            warnings.filterwarnings("ignore", message=".*development server.*")

            # Set werkzeug logger to only show errors (suppresses WARNING level)
            werkzeug_logger = logging.getLogger("werkzeug")
            werkzeug_logger.setLevel(logging.ERROR)

            if quiet:
                # Suppress Flask's startup banner messages
                import flask.cli

                flask.cli.show_server_banner = lambda *args, **kwargs: None

                # Disable werkzeug and Flask loggers completely
                werkzeug_logger.disabled = True
                app.logger.disabled = True
                app.logger.setLevel(logging.CRITICAL)

            # Use a simple connection pool to limit total connections
            # DuckDB has a global limit on database attachments
            import threading
            from queue import Queue

            # Create a pool of connections (limit to avoid "database already attached" errors)
            max_connections = 4  # Conservative limit
            connection_pool = Queue(maxsize=max_connections)
            pool_lock = threading.Lock()

            def create_connection():
                """Create a new DuckDB connection with extensions loaded."""
                con = duckdb.connect(database_path, read_only=True)
                try:
                    con.execute("INSTALL spatial;")
                    con.execute("LOAD spatial;")
                    con.execute("INSTALL httpfs;")
                    con.execute("LOAD httpfs;")
                    con.execute("SET s3_region='us-west-2';")
                    con.execute("SET s3_url_style='path';")
                except Exception:
                    pass
                return con

            # Pre-populate the pool
            for _ in range(max_connections):
                connection_pool.put(create_connection())

            # Register the connection pool in the global registry
            _duckdb_connection_pools[database_path] = {
                "pool": connection_pool,
                "lock": pool_lock,
                "max_connections": max_connections,
            }

            def get_db_connection():
                """Get a connection from the pool."""
                return connection_pool.get()

            def return_db_connection(con):
                """Return a connection to the pool."""
                connection_pool.put(con)

            @app.route("/tiles/<int:z>/<int:x>/<int:y>.pbf", methods=["GET", "OPTIONS"])
            def get_tile(z, x, y):
                """Serve vector tiles from DuckDB."""
                # Handle CORS preflight requests
                from flask import request

                if request.method == "OPTIONS":
                    response = Response()
                    if cors:
                        response.headers["Access-Control-Allow-Origin"] = "*"
                        response.headers["Access-Control-Allow-Methods"] = (
                            "GET, HEAD, OPTIONS"
                        )
                        response.headers["Access-Control-Allow-Headers"] = (
                            "Content-Type"
                        )
                    return response

                # Check if zoom level is below minimum threshold
                if min_zoom is not None and z < min_zoom:
                    # Return empty tile for zoom levels below min_zoom
                    response = Response(b"", mimetype="application/x-protobuf")
                    if cors:
                        response.headers["Access-Control-Allow-Origin"] = "*"
                        response.headers["Access-Control-Allow-Methods"] = (
                            "GET, HEAD, OPTIONS"
                        )
                        response.headers["Access-Control-Allow-Headers"] = (
                            "Content-Type"
                        )
                    return response

                # Get connection from pool
                con = get_db_connection()

                try:
                    # Build the MVT query
                    # Get all columns except geometry for properties
                    if properties is None:
                        # Get column names and types, filtering for MVT-compatible types
                        # ST_AsMVT only supports: VARCHAR, FLOAT, DOUBLE, INTEGER, BIGINT, BOOLEAN
                        columns = con.execute(
                            f"""
                            SELECT column_name, data_type
                            FROM information_schema.columns
                            WHERE table_name = '{table_name}'
                            AND column_name != '{geom_column}'
                            AND data_type IN ('VARCHAR', 'FLOAT', 'DOUBLE', 'INTEGER', 'BIGINT', 'BOOLEAN',
                                             'SMALLINT', 'TINYINT', 'UBIGINT', 'UINTEGER', 'USMALLINT', 'UTINYINT',
                                             'HUGEINT', 'REAL', 'TEXT')
                            """
                        ).fetchall()
                        prop_list = [col[0] for col in columns]
                    else:
                        prop_list = properties

                    # Build property assignments for ST_AsMVT
                    if prop_list:
                        prop_assigns = ", ".join(
                            [f'"{prop}": "{prop}"' for prop in prop_list]
                        )
                        # Add comma after properties if they exist
                        prop_assigns = prop_assigns + ","
                    else:
                        prop_assigns = ""

                    # Determine geometry expression
                    # If src_crs is provided and not already Web Mercator, transform on-the-fly
                    if src_crs and src_crs.upper() not in ["EPSG:3857", "3857"]:
                        # Transform geometry from source CRS to Web Mercator for tile serving
                        geom_expr = f"ST_Transform(ST_GeomFromWKB(ST_AsWKB({geom_column})), '{src_crs}', 'EPSG:3857', true)"
                    else:
                        # Use geometry as-is (already in Web Mercator or no CRS specified)
                        geom_expr = geom_column

                    # Query to generate the tile
                    query = f"""
                        SELECT ST_AsMVT({{
                            {prop_assigns}
                            "geom": ST_AsMVTGeom(
                                {geom_expr},
                                ST_Extent(ST_TileEnvelope($1, $2, $3))
                            )
                        }})
                        FROM {table_name}
                        WHERE ST_Intersects({geom_expr}, ST_TileEnvelope($1, $2, $3))
                    """

                    with con.cursor() as cursor:
                        tile_blob = cursor.execute(query, [z, x, y]).fetchone()

                    tile = tile_blob[0] if tile_blob and tile_blob[0] else b""
                    response = Response(tile, mimetype="application/x-protobuf")
                    if cors:
                        response.headers["Access-Control-Allow-Origin"] = "*"
                        response.headers["Access-Control-Allow-Methods"] = (
                            "GET, HEAD, OPTIONS"
                        )
                        response.headers["Access-Control-Allow-Headers"] = (
                            "Content-Type"
                        )
                    return response

                except Exception as e:
                    if not quiet:
                        print(f"Error generating tile {z}/{x}/{y}: {e}")
                        import traceback

                        traceback.print_exc()
                    response = Response(b"", mimetype="application/x-protobuf")
                    if cors:
                        response.headers["Access-Control-Allow-Origin"] = "*"
                        response.headers["Access-Control-Allow-Methods"] = (
                            "GET, HEAD, OPTIONS"
                        )
                        response.headers["Access-Control-Allow-Headers"] = (
                            "Content-Type"
                        )
                    return response, 500
                finally:
                    # Always return connection to pool
                    return_db_connection(con)

            @app.route("/")
            def index():
                """Serve information about the tile server."""
                return f"""
                <html>
                <head><title>DuckDB Vector Tile Server</title></head>
                <body>
                    <h2>DuckDB Vector Tile Server</h2>
                    <p>Tile endpoint: <code>/tiles/{{z}}/{{x}}/{{y}}.pbf</code></p>
                    <p>Database: {database_path}</p>
                    <p>Table: {table_name}</p>
                    <p>Geometry column: {geom_column}</p>
                </body>
                </html>
                """

            if not quiet:
                print(f"DuckDB tile server running at http://127.0.0.1:{actual_port}/")
                print(
                    f"Tiles available at: http://127.0.0.1:{actual_port}/tiles/{{z}}/{{x}}/{{y}}.pbf"
                )

            # Run Flask app
            # Bind to 0.0.0.0 to allow access from jupyter-server-proxy in remote environments
            # In local environments, this is still secure as it's behind the firewall
            app.run(host="0.0.0.0", port=actual_port, threaded=True, use_reloader=False)

        except ImportError as e:
            if not quiet:
                print(f"Error importing module: {e}")
                print(
                    "Please install required packages: pip install duckdb flask flask-cors"
                )
        except Exception as e:
            if not quiet:
                print(f"An error occurred: {e}")

    if background:
        import threading

        t = threading.Thread(target=run_flask, daemon=True)
        t.start()
        # Give the server a moment to start
        # In remote environments, we need a bit more time for the server to be ready
        import time

        time.sleep(1.5)
    else:
        run_flask()

    return actual_port


def close_duckdb_connections(database_path: str = None, quiet: bool = True):
    """
    Close DuckDB connections for a specific database or all databases.

    This function closes all connections in the connection pool for the specified
    database, allowing other programs to access the database file. This is useful
    when you're done using the database and want to release the file lock.

    Args:
        database_path (str, optional): Path to the DuckDB database file.
            If None, closes connections for all databases. Defaults to None.
        quiet (bool, optional): If True, suppress status messages. Defaults to True.

    Returns:
        None

    Example:
        >>> import leafmap
        >>> # After using add_duckdb_layer
        >>> m = leafmap.Map()
        >>> m.add_duckdb_layer("tiles.db")
        >>> # Later, close the connections
        >>> leafmap.close_duckdb_connections()
        >>> # Or close connections for a specific database
        >>> leafmap.close_duckdb_connections("tiles.db")
    """
    global _duckdb_connection_pools

    if database_path is None:
        # Close all connections
        databases_to_close = list(_duckdb_connection_pools.keys())
    else:
        # Close connections for specific database
        databases_to_close = (
            [database_path] if database_path in _duckdb_connection_pools else []
        )

    for db_path in databases_to_close:
        pool_info = _duckdb_connection_pools.get(db_path)
        if pool_info:
            connection_pool = pool_info["pool"]
            pool_lock = pool_info["lock"]
            max_connections = pool_info["max_connections"]

            if not quiet:
                print(f"Closing DuckDB connections for: {db_path}")

            # Acquire the pool lock before closing connections
            with pool_lock:
                # Close all connections in the pool
                closed_count = 0
                while not connection_pool.empty():
                    try:
                        con = connection_pool.get_nowait()
                        con.close()
                        closed_count += 1
                    except Exception as e:
                        if not quiet:
                            print(f"Error closing connection: {e}")

                # Remove from registry
                del _duckdb_connection_pools[db_path]

                if not quiet:
                    print(
                        f"Closed {closed_count}/{max_connections} connections for {db_path}"
                    )

    if not quiet and len(databases_to_close) == 0:
        if database_path is None:
            print("No active DuckDB connections to close.")
        else:
            print(f"No active connections found for database: {database_path}")


def vector_to_mbtiles(
    source_path: str, target_path: str, max_zoom: int = 5, name: str = None, **kwargs
) -> None:
    """
    Convert a vector dataset to MBTiles format using the ogr2ogr command-line tool.

    Args:
        source_path (str): The path to the source vector dataset (GeoPackage, Shapefile, etc.).
        target_path (str): The path to the target MBTiles file to be created.
        max_zoom (int, optional): The maximum zoom level for the MBTiles dataset. Defaults to 5.
        name (str, optional): The name of the MBTiles dataset. Defaults to None.
        **kwargs: Additional options to be passed as keyword arguments. These options will be used as -dsco options
                  when calling ogr2ogr. See https://gdal.org/drivers/raster/mbtiles.html for a list of options.

    Returns:
        None

    Raises:
        subprocess.CalledProcessError: If the ogr2ogr command fails to execute.

    Example:
        source_path = "countries.gpkg"
        target_path = "target.mbtiles"
        name = "My MBTiles"
        max_zoom = 5
        vector_to_mbtiles(source_path, target_path, name=name, max_zoom=max_zoom)
    """
    import subprocess

    command = [
        "ogr2ogr",
        "-f",
        "MBTILES",
        target_path,
        source_path,
        "-dsco",
        f"MAXZOOM={max_zoom}",
    ]

    if name:
        command.extend(["-dsco", f"NAME={name}"])

    for key, value in kwargs.items():
        command.extend(["-dsco", f"{key.upper()}={value}"])

    try:
        subprocess.run(command, check=True)
    except subprocess.CalledProcessError as e:
        raise e


def geojson_to_mbtiles(
    input_file: str,
    output_file: str,
    layer_name: Optional[str] = None,
    options: Optional[List[str]] = None,
    quiet: bool = False,
) -> Optional[str]:
    """
    Converts vector data to .mbtiles using Tippecanoe.

    Args:
        input_file (str): Path to the input vector data file (e.g., .geojson).
        output_file (str): Path to the output .mbtiles file.
        layer_name (Optional[str]): Optional name for the layer. Defaults to None.
        options (Optional[List[str]]): List of additional arguments for tippecanoe. For example '-zg' for auto maxzoom. Defaults to None.
        quiet (bool): If True, suppress the log output. Defaults to False.

    Returns:
        Output from the Tippecanoe command, or None if there was an error or if Tippecanoe is not installed.

    Raises:
        subprocess.CalledProcessError: If there's an error executing the tippecanoe command.
    """

    import shutil
    import subprocess

    # Check if tippecanoe exists
    if shutil.which("tippecanoe") is None:
        print("Error: tippecanoe is not installed.")
        print("You can install it using conda with the following command:")
        print("conda install -c conda-forge tippecanoe")
        return None

    command = ["tippecanoe", "-o", output_file]

    # Add layer name specification if provided
    if layer_name:
        command.extend(["-L", f"{layer_name}:{input_file}"])
    else:
        command.append(input_file)

    # Append additional arguments if provided
    if options:
        command.extend(options)

    try:
        process = subprocess.Popen(
            command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True
        )

        if not quiet:
            for line in process.stdout:
                print(line, end="")

        exit_code = process.wait()
        if exit_code != 0:
            raise subprocess.CalledProcessError(exit_code, command)

    except subprocess.CalledProcessError as e:
        print(f"\nError executing tippecanoe: {e}")
        return None

    return "Tippecanoe process completed successfully."


def mbtiles_to_pmtiles(
    input_file: str, output_file: str, max_zoom: int = 99
) -> Optional[None]:
    """
    Converts mbtiles to pmtiles using the pmtiles package.

    Args:
        input_file (str): Path to the input .mbtiles file.
        output_file (str): Path to the output .pmtiles file.
        max_zoom (int): Maximum zoom level for the conversion. Defaults to 99.

    Returns:
        None upon successful completion or when the pmtiles package is not installed.

    """

    import pmtiles.convert as convert

    convert.mbtiles_to_pmtiles(input_file, output_file, maxzoom=max_zoom)


def vector_to_pmtiles(
    source_path: str, target_path: str, max_zoom: int = 15, name: str = None, **kwargs
) -> None:
    """
    Converts a vector file to PMTiles format.

    Args:
        source_path (str): Path to the source vector file.
        target_path (str): Path to the target PMTiles file.
        max_zoom (int, optional): Maximum zoom level for the PMTiles. Defaults to 5.
        name (str, optional): Name of the PMTiles dataset. Defaults to None.
        **kwargs: Additional keyword arguments to be passed to the underlying conversion functions.

    Raises:
        ValueError: If the target file does not have a .pmtiles extension.

    Returns:
        None
    """
    if not target_path.endswith(".pmtiles"):
        raise ValueError("Error: target file must be a .pmtiles file.")
    import subprocess

    command = [
        "ogr2ogr",
        "-skipfailures",
        "-f",
        "PMTiles",
        "-dsco",
        f"MAXZOOM={max_zoom}",
        target_path,
        source_path,
    ]

    if name:
        command.extend([f"NAME={name}"])

    for key, value in kwargs.items():
        command.extend([f"{key.upper()}={value}"])

    try:
        subprocess.run(command, check=True)
    except subprocess.CalledProcessError as e:
        raise e


def geojson_to_pmtiles(
    input_file: str,
    output_file: Optional[str] = None,
    layer_name: Optional[str] = None,
    projection: Optional[str] = "EPSG:4326",
    overwrite: bool = False,
    options: Optional[List[str]] = None,
    quiet: bool = False,
) -> Optional[str]:
    """
    Converts vector data to PMTiles using Tippecanoe.

    Args:
        input_file (str): Path to the input vector data file (e.g., .geojson).
        output_file (str): Path to the output .mbtiles file.
        layer_name (Optional[str]): Optional name for the layer. Defaults to None.
        projection (Optional[str]): Projection for the output PMTiles file. Defaults to "EPSG:4326".
        overwrite (bool): If True, overwrite the existing output file. Defaults to False.
        options (Optional[List[str]]): List of additional arguments for tippecanoe. Defaults to None.
            To reduce the size of the output file, use '-zg' or '-z max-zoom'.
        quiet (bool): If True, suppress the log output. Defaults to False.

    Returns:
        Output from the Tippecanoe command, or None if there was an error or if Tippecanoe is not installed.

    Raises:
        subprocess.CalledProcessError: If there's an error executing the tippecanoe command.
    """

    import shutil
    import subprocess

    # Check if tippecanoe exists
    if shutil.which("tippecanoe") is None:
        print("Error: tippecanoe is not installed.")
        print("You can install it using conda with the following command:")
        print("conda install -c conda-forge tippecanoe")
        return None

    if output_file is None:
        output_file = os.path.splitext(input_file)[0] + ".pmtiles"

    if not output_file.endswith(".pmtiles"):
        raise ValueError("Error: output file must be a .pmtiles file.")

    command = ["tippecanoe", "-o", output_file]

    # Add layer name specification if provided
    if layer_name:
        command.extend(["-L", f"{layer_name}:{input_file}"])
    else:
        command.append(input_file)

    command.extend(["--projection", projection])

    if options is None:
        options = []

    if overwrite:
        command.append("--force")

    # Append additional arguments if provided
    if options:
        command.extend(options)

    try:
        process = subprocess.Popen(
            command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True
        )

        if not quiet:
            for line in process.stdout:
                print(line, end="")

        exit_code = process.wait()
        if exit_code != 0:
            raise subprocess.CalledProcessError(exit_code, command)

    except subprocess.CalledProcessError as e:
        print(f"\nError executing tippecanoe: {e}")
        return None

    return "Tippecanoe process completed successfully."


def pmtiles_header(input_file: str):
    """
    Fetch the header information from a local or remote .pmtiles file.

    This function retrieves the header from a PMTiles file, either local or hosted remotely.
    It deserializes the header and calculates the center and bounds of the tiles from the
    given metadata in the header.

    Args:
        input_file (str): Path to the .pmtiles file, or its URL if the file is hosted remotely.

    Returns:
        A dictionary containing the header information, including center and bounds.

    Raises:
        ImportError: If the pmtiles library is not installed.
        ValueError: If the input file is not a .pmtiles file or if it does not exist.

    Example:
        >>> header = pmtiles_header("https://example.com/path/to/tiles.pmtiles")
        >>> print(header["center"])
        [52.5200, 13.4050]

    Note:
        If fetching a remote PMTiles file, this function only downloads the first 127 bytes
        of the file to retrieve the header.
    """

    from urllib.parse import urlparse

    import requests

    try:
        from pmtiles.reader import MmapSource, Reader
        from pmtiles.tile import deserialize_header
    except ImportError:
        print(
            "pmtiles is not installed. Please install it using `pip install pmtiles`."
        )
        return
    if not urlparse(input_file).path.endswith(".pmtiles"):
        raise ValueError("Input file must be a .pmtiles file.")

    if input_file.startswith("http"):
        # Fetch only the first 127 bytes
        headers = {"Range": "bytes=0-127"}
        response = requests.get(input_file, headers=headers)
        header = deserialize_header(response.content)

    else:
        if not os.path.exists(input_file):
            raise ValueError(f"Input file {input_file} does not exist.")

        with open(input_file, "rb") as f:
            reader = Reader(MmapSource(f))
            header = reader.header()

    header["center"] = [header["center_lat_e7"] / 1e7, header["center_lon_e7"] / 1e7]
    header["bounds"] = [
        header["min_lon_e7"] / 1e7,
        header["min_lat_e7"] / 1e7,
        header["max_lon_e7"] / 1e7,
        header["max_lat_e7"] / 1e7,
    ]

    return header


def pmtiles_metadata(input_file: str) -> Dict[str, Union[str, int, List[str]]]:
    """
    Fetch the metadata from a local or remote .pmtiles file.

    This function retrieves metadata from a PMTiles file, whether it's local or hosted remotely.
    If it's remote, the function fetches the header to determine the range of bytes to download
    for obtaining the metadata. It then reads the metadata and extracts the layer names.

    Args:
        input_file (str): Path to the .pmtiles file, or its URL if the file is hosted remotely.

    Returns:
        A dictionary containing the metadata information, including layer names.

    Raises:
        ImportError: If the pmtiles library is not installed.
        ValueError: If the input file is not a .pmtiles file or if it does not exist.

    Example:
        >>> metadata = pmtiles_metadata("https://example.com/path/to/tiles.pmtiles")
        >>> print(metadata["layer_names"])
        ['buildings', 'roads']

    Note:
        If fetching a remote PMTiles file, this function may perform multiple requests to minimize
        the amount of data downloaded.
    """

    import json
    from urllib.parse import urlparse

    import requests

    try:
        from pmtiles.reader import MemorySource, MmapSource, Reader
    except ImportError:
        print(
            "pmtiles is not installed. Please install it using `pip install pmtiles`."
        )
        return

    # ignore uri parameters when checking file suffix
    if not urlparse(input_file).path.endswith(".pmtiles"):
        raise ValueError("Input file must be a .pmtiles file.")

    header = pmtiles_header(input_file)
    metadata_offset = header["metadata_offset"]
    metadata_length = header["metadata_length"]

    if input_file.startswith("http"):
        headers = {"Range": f"bytes=0-{metadata_offset + metadata_length}"}
        response = requests.get(input_file, headers=headers)
        content = MemorySource(response.content)
        metadata = Reader(content).metadata()
    else:
        with open(input_file, "rb") as f:
            reader = Reader(MmapSource(f))
            metadata = reader.metadata()
            if "json" in metadata:
                metadata["vector_layers"] = json.loads(metadata["json"])[
                    "vector_layers"
                ]

    vector_layers = metadata["vector_layers"]
    layer_names = [layer["id"] for layer in vector_layers]

    if "tilestats" in metadata:
        geometries = [layer["geometry"] for layer in metadata["tilestats"]["layers"]]
        metadata["geometries"] = geometries

    metadata["layer_names"] = layer_names
    metadata["center"] = header["center"]
    metadata["bounds"] = header["bounds"]
    return metadata


def pmtiles_style(
    url: str,
    layers: Optional[Union[str, List[str]]] = None,
    cmap: str = "Set3",
    n_class: Optional[int] = None,
    opacity: float = 0.5,
    circle_radius: int = 5,
    line_width: int = 1,
    attribution: str = "PMTiles",
    **kwargs: Any,
):
    """
    Generates a Mapbox style JSON for rendering PMTiles data.

    Args:
        url (str): The URL of the PMTiles file.
        layers (str or list[str], optional): The layers to include in the style. If None, all layers will be included.
            Defaults to None.
        cmap (str, optional): The color map to use for styling the layers. Defaults to "Set3".
        n_class (int, optional): The number of classes to use for styling. If None, the number of classes will be
            determined automatically based on the color map. Defaults to None.
        opacity (float, optional): The fill opacity for polygon layers. Defaults to 0.5.
        circle_radius (int, optional): The circle radius for point layers. Defaults to 5.
        line_width (int, optional): The line width for line layers. Defaults to 1.
        attribution (str, optional): The attribution text for the data source. Defaults to "PMTiles".

    Returns:
        The Mapbox style JSON.

    Raises:
        ValueError: If the layers argument is not a string or a list.
        ValueError: If a layer specified in the layers argument does not exist in the PMTiles file.
    """

    if cmap == "Set3":
        palette = [
            "#8dd3c7",
            "#ffffb3",
            "#bebada",
            "#fb8072",
            "#80b1d3",
            "#fdb462",
            "#b3de69",
            "#fccde5",
            "#d9d9d9",
            "#bc80bd",
            "#ccebc5",
            "#ffed6f",
        ]
    elif isinstance(cmap, list):
        palette = cmap
    else:
        from .colormaps import get_palette

        palette = ["#" + c for c in get_palette(cmap, n_class)]

    n_class = len(palette)

    metadata = pmtiles_metadata(url)
    layer_names = metadata["layer_names"]

    style = {
        "version": 8,
        "sources": {
            "source": {
                "type": "vector",
                "url": "pmtiles://" + url,
                "attribution": attribution,
            }
        },
        "layers": [],
    }

    if layers is None:
        layers = layer_names
    elif isinstance(layers, str):
        layers = [layers]
    elif isinstance(layers, list):
        for layer in layers:
            if layer not in layer_names:
                raise ValueError(f"Layer {layer} does not exist in the PMTiles file.")
    else:
        raise ValueError("The layers argument must be a string or a list.")

    for i, layer_name in enumerate(layers):
        layer_point = {
            "id": f"{layer_name}_point",
            "source": "source",
            "source-layer": layer_name,
            "type": "circle",
            "paint": {
                "circle-color": palette[i % n_class],
                "circle-radius": circle_radius,
            },
            "filter": ["==", ["geometry-type"], "Point"],
        }

        layer_stroke = {
            "id": f"{layer_name}_stroke",
            "source": "source",
            "source-layer": layer_name,
            "type": "line",
            "paint": {
                "line-color": palette[i % n_class],
                "line-width": line_width,
            },
            "filter": ["==", ["geometry-type"], "LineString"],
        }

        layer_fill = {
            "id": f"{layer_name}_fill",
            "source": "source",
            "source-layer": layer_name,
            "type": "fill",
            "paint": {
                "fill-color": palette[i % n_class],
                "fill-opacity": opacity,
            },
            "filter": ["==", ["geometry-type"], "Polygon"],
        }

        style["layers"].extend([layer_point, layer_stroke, layer_fill])

    return style


def raster_to_vector(
    source, output, simplify_tolerance=None, dst_crs=None, open_args={}, **kwargs
):
    """Vectorize a raster dataset.

    Args:
        source (str): The path to the tiff file.
        output (str): The path to the vector file.
        simplify_tolerance (float, optional): The maximum allowed geometry displacement.
            The higher this value, the smaller the number of vertices in the resulting geometry.
    """
    import geopandas as gpd
    import rasterio
    import shapely
    from rasterio import features

    with rasterio.open(source, **open_args) as src:
        band = src.read()

        mask = band != 0
        shapes = features.shapes(band, mask=mask, transform=src.transform)

    fc = [
        {"geometry": shapely.geometry.shape(shape), "properties": {"value": value}}
        for shape, value in shapes
    ]
    if simplify_tolerance is not None:
        for i in fc:
            i["geometry"] = i["geometry"].simplify(tolerance=simplify_tolerance)

    gdf = gpd.GeoDataFrame.from_features(fc)
    if src.crs is not None:
        gdf.set_crs(crs=src.crs, inplace=True)

    if dst_crs is not None:
        gdf = gdf.to_crs(dst_crs)

    gdf.to_file(output, **kwargs)


def overlay_images(
    image1,
    image2,
    alpha=0.5,
    backend="TkAgg",
    height_ratios=[10, 1],
    show_args1={},
    show_args2={},
):
    """Overlays two images using a slider to control the opacity of the top image.

    Args:
        image1 (str | np.ndarray): The first input image at the bottom represented as a NumPy array or the path to the image.
        image2 (_type_): The second input image on top represented as a NumPy array or the path to the image.
        alpha (float, optional): The alpha value of the top image. Defaults to 0.5.
        backend (str, optional): The backend of the matplotlib plot. Defaults to "TkAgg".
        height_ratios (list, optional): The height ratios of the two subplots. Defaults to [10, 1].
        show_args1 (dict, optional): The keyword arguments to pass to the imshow() function for the first image. Defaults to {}.
        show_args2 (dict, optional): The keyword arguments to pass to the imshow() function for the second image. Defaults to {}.

    """
    import sys

    import matplotlib
    import matplotlib.pyplot as plt
    import matplotlib.widgets as mpwidgets

    if "google.colab" in sys.modules:
        backend = "inline"
        print(
            "The TkAgg backend is not supported in Google Colab. The overlay_images function will not work on Colab."
        )
        return

    matplotlib.use(backend)

    if isinstance(image1, str):
        if image1.startswith("http"):
            image1 = download_file(image1)

        if not os.path.exists(image1):
            raise ValueError(f"Input path {image1} does not exist.")

    if isinstance(image2, str):
        if image2.startswith("http"):
            image2 = download_file(image2)

        if not os.path.exists(image2):
            raise ValueError(f"Input path {image2} does not exist.")

    # Load the two images
    x = plt.imread(image1)
    y = plt.imread(image2)

    # Create the plot
    fig, (ax0, ax1) = plt.subplots(2, 1, gridspec_kw={"height_ratios": height_ratios})
    img0 = ax0.imshow(x, **show_args1)
    img1 = ax0.imshow(y, alpha=alpha, **show_args2)

    # Define the update function
    def update(value):
        img1.set_alpha(value)
        fig.canvas.draw_idle()

    # Create the slider
    slider0 = mpwidgets.Slider(ax=ax1, label="alpha", valmin=0, valmax=1, valinit=alpha)
    slider0.on_changed(update)

    # Display the plot
    plt.show()


def blend_images(
    img1,
    img2,
    alpha=0.5,
    output=False,
    show=True,
    figsize=(12, 10),
    axis="off",
    **kwargs: Any,
) -> Any:
    """
    Blends two images together using the addWeighted function from the OpenCV library.

    Args:
        img1 (numpy.ndarray): The first input image on top represented as a NumPy array.
        img2 (numpy.ndarray): The second input image at the bottom represented as a NumPy array.
        alpha (float): The weighting factor for the first image in the blend. By default, this is set to 0.5.
        output (str, optional): The path to the output image. Defaults to False.
        show (bool, optional): Whether to display the blended image. Defaults to True.
        figsize (tuple, optional): The size of the figure. Defaults to (12, 10).
        axis (str, optional): The axis of the figure. Defaults to "off".
        **kwargs (Any): Additional keyword arguments to pass to the cv2.addWeighted() function.

    Returns:
        The blended image as a NumPy array.
    """
    import matplotlib.pyplot as plt
    import numpy as np

    try:
        import cv2
    except ImportError:
        raise ImportError("The blend_images function requires the OpenCV library.")

    # Resize the images to have the same dimensions
    if isinstance(img1, str):
        if img1.startswith("http"):
            img1 = download_file(img1)

        if not os.path.exists(img1):
            raise ValueError(f"Input path {img1} does not exist.")

        img1 = cv2.imread(img1)

    if isinstance(img2, str):
        if img2.startswith("http"):
            img2 = download_file(img2)

        if not os.path.exists(img2):
            raise ValueError(f"Input path {img2} does not exist.")

        img2 = cv2.imread(img2)

    if img1.dtype == np.float32:
        img1 = (img1 * 255).astype(np.uint8)

    if img2.dtype == np.float32:
        img2 = (img2 * 255).astype(np.uint8)

    if img1.dtype != img2.dtype:
        img2 = img2.astype(img1.dtype)

    img1 = cv2.resize(img1, (img2.shape[1], img2.shape[0]))

    # Blend the images using the addWeighted function
    beta = 1 - alpha
    blend_img = cv2.addWeighted(img1, alpha, img2, beta, 0, **kwargs)

    if output:
        array_to_image(blend_img, output, img2)

    if show:
        plt.figure(figsize=figsize)
        plt.imshow(blend_img)
        plt.axis(axis)
        plt.show()
    else:
        return blend_img


def regularize(source, output=None, crs="EPSG:4326", **kwargs: Any):
    """Regularize a polygon GeoDataFrame.

    Args:
        source (str | gpd.GeoDataFrame): The input file path or a GeoDataFrame.
        output (str, optional): The output file path. Defaults to None.


    Returns:
        The output GeoDataFrame.
    """
    import geopandas as gpd

    if isinstance(source, str):
        gdf = gpd.read_file(source)
    elif isinstance(source, gpd.GeoDataFrame):
        gdf = source
    else:
        raise ValueError("The input source must be a GeoDataFrame or a file path.")

    polygons = gdf.geometry.apply(lambda geom: geom.minimum_rotated_rectangle)
    result = gpd.GeoDataFrame(geometry=polygons, data=gdf.drop("geometry", axis=1))

    if crs is not None:
        result.to_crs(crs, inplace=True)
    if output is not None:
        result.to_file(output, **kwargs)
    else:
        return result


def get_gdal_drivers() -> List[str]:
    """Get a list of available driver names in the GDAL library.

    Returns:
        A list of available driver names.
    """
    from osgeo import ogr

    driver_list = []

    # Iterate over all registered drivers
    for i in range(ogr.GetDriverCount()):
        driver = ogr.GetDriver(i)
        driver_name = driver.GetName()
        driver_list.append(driver_name)

    return driver_list


def get_gdal_file_extension(driver_name: str) -> Optional[str]:
    """Get the file extension corresponding to a driver name in the GDAL library.

    Args:
        driver_name (str): The name of the driver.

    Returns:
        The file extension corresponding to the driver name, or None if the driver is not found or does not have a specific file extension.
    """
    from osgeo import ogr

    driver = ogr.GetDriverByName(driver_name)
    if driver is None:
        drivers = get_gdal_drivers()
        raise ValueError(
            f"Driver {driver_name} not found. Available drivers: {drivers}"
        )

    metadata = driver.GetMetadata()
    if "DMD_EXTENSION" in metadata:
        file_extension = driver.GetMetadataItem("DMD_EXTENSION")
    else:
        file_extensions = driver.GetMetadataItem("DMD_EXTENSIONS")
        if file_extensions == "json geojson":
            file_extension = "geojson"
        else:
            file_extension = file_extensions.split()[0].lower()

    return file_extension


def gdb_to_vector(
    gdb_path: str,
    out_dir: str,
    layers: Optional[List[str]] = None,
    filenames: Optional[List[str]] = None,
    gdal_driver: str = "GPKG",
    file_extension: Optional[str] = None,
    overwrite: bool = False,
    quiet=False,
    **kwargs: Any,
) -> None:
    """Converts layers from a File Geodatabase (GDB) to a vector format.

    Args:
        gdb_path (str): The path to the File Geodatabase (GDB).
        out_dir (str): The output directory to save the converted files.
        layers (Optional[List[str]]): A list of layer names to convert. If None, all layers will be converted. Default is None.
        filenames (Optional[List[str]]): A list of output file names. If None, the layer names will be used as the file names. Default is None.
        gdal_driver (str): The GDAL driver name for the output vector format. Default is "GPKG".
        file_extension (Optional[str]): The file extension for the output files. If None, it will be determined automatically based on the gdal_driver. Default is None.
        overwrite (bool): Whether to overwrite the existing output files. Default is False.
        quiet (bool): If True, suppress the log output. Defaults to False.

    Returns:
        None
    """
    from osgeo import ogr

    # Open the GDB
    gdb_driver = ogr.GetDriverByName("OpenFileGDB")
    gdb_dataset = gdb_driver.Open(gdb_path, 0)

    # Get the number of layers in the GDB
    layer_count = gdb_dataset.GetLayerCount()

    if isinstance(layers, str):
        layers = [layers]

    if isinstance(filenames, str):
        filenames = [filenames]

    if filenames is not None:
        if len(filenames) != len(layers):
            raise ValueError("The length of filenames must match the length of layers.")

    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    ii = 0
    # Iterate over the layers
    for i in range(layer_count):
        layer = gdb_dataset.GetLayerByIndex(i)
        feature_class_name = layer.GetName()

        if layers is not None:
            if feature_class_name not in layers:
                continue

        if file_extension is None:
            file_extension = get_gdal_file_extension(gdal_driver)

        # Create the output file path
        if filenames is not None:
            output_file = os.path.join(out_dir, filenames[ii] + "." + file_extension)
            ii += 1
        else:
            output_file = os.path.join(
                out_dir, feature_class_name + "." + file_extension
            )

        if os.path.exists(output_file) and not overwrite:
            print(f"File {output_file} already exists. Skipping...")
            continue
        else:
            if not quiet:
                print(f"Converting layer {feature_class_name} to {output_file}...")

        # Create the output driver
        output_driver = ogr.GetDriverByName(gdal_driver)
        output_dataset = output_driver.CreateDataSource(output_file)

        # Copy the input layer to the output format
        output_dataset.CopyLayer(layer, feature_class_name)

        output_dataset = None

    # Close the GDB dataset
    gdb_dataset = None


def gdb_layer_names(gdb_path: str) -> List[str]:
    """Get a list of layer names in a File Geodatabase (GDB).

    Args:
        gdb_path (str): The path to the File Geodatabase (GDB).

    Returns:
        A list of layer names in the GDB.
    """

    from osgeo import ogr

    # Open the GDB
    gdb_driver = ogr.GetDriverByName("OpenFileGDB")
    gdb_dataset = gdb_driver.Open(gdb_path, 0)

    # Get the number of layers in the GDB
    layer_count = gdb_dataset.GetLayerCount()
    # Iterate over the layers
    layer_names = []
    for i in range(layer_count):
        layer = gdb_dataset.GetLayerByIndex(i)
        feature_class_name = layer.GetName()
        layer_names.append(feature_class_name)

    # Close the GDB dataset
    gdb_dataset = None
    return layer_names


def vector_to_parquet(
    source: str, output: str, crs=None, overwrite=False, **kwargs
) -> None:
    """
    Convert a GeoDataFrame or a file containing vector data to Parquet format.

    Args:
        source (Union[gpd.GeoDataFrame, str]): The source data to convert. It can be either a GeoDataFrame
            or a file path to the vector data file.
        output (str): The file path where the Parquet file will be saved.
        crs (str, optional): The coordinate reference system (CRS) to use for the output file. Defaults to None.
        overwrite (bool): Whether to overwrite the existing output file. Default is False.
        **kwargs: Additional keyword arguments to be passed to the `to_parquet` function of GeoDataFrame.

    Returns:
        None
    """

    import geopandas as gpd

    if os.path.exists(output) and not overwrite:
        print(f"File {output} already exists. Skipping...")
        return

    if isinstance(source, gpd.GeoDataFrame):
        gdf = source
    else:
        gdf = gpd.read_file(source)

    if crs is not None:
        gdf = gdf.to_crs(crs)

    out_dir = os.path.dirname(os.path.abspath(output))
    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    gdf.to_parquet(output, **kwargs)


def vector_to_parquet_batch(
    input_dir, output_dir=None, file_ext=".geojson", **kwargs: Any
):
    """
    Converts all vector files in a directory to Parquet format in batch.

    Args:
        input_dir (str): The directory containing the input vector files.
        output_dir (str, optional): The directory to save the converted Parquet files.
            If not provided, the input directory will be used. Defaults to None.
        file_ext (str): The file extension of the input vector files (e.g., ".geojson"). Defaults to ".geojson".
        **kwargs: Additional keyword arguments to be passed to the glob.glob function for file matching.

    Returns:
        None

    Example:
        >>> vector_to_parquet_batch("input_directory", "output_directory", ".geojson")
    """
    import glob

    import duckdb

    # Set output directory
    if output_dir is None:
        output_dir = input_dir

    # Create output directory if it doesn't exist
    os.makedirs(output_dir, exist_ok=True)

    # Initialize DuckDB connection
    conn = duckdb.connect(database=":memory:")

    # Install and load required extensions
    conn.execute("INSTALL spatial")
    conn.execute("LOAD spatial")

    # Get all GeoJSON files in the input directory
    files = glob.glob(os.path.join(input_dir, f"*.{file_ext.lstrip('.')}"), **kwargs)

    # Process each file
    for index, file in enumerate(files):
        # Get base filename without extension
        base_name = os.path.basename(file)
        file_name_without_ext = os.path.splitext(base_name)[0]

        # Define output path
        parquet_file = os.path.join(output_dir, f"{file_name_without_ext}.parquet")

        print(f"Converting {index + 1}/{len(files)}: {base_name} to Parquet...")

        try:
            # Execute the conversion
            conn.execute(
                f"""
                COPY (
                    SELECT * FROM ST_Read('{file}')
                ) TO '{parquet_file}' (FORMAT PARQUET)
            """
            )
        except Exception as e:
            print(f"Error converting {base_name}: {str(e)}")

    # Close connection
    conn.close()
    print("All conversions complete!")


def vector_to_gpkg_batch(
    input_dir, output_dir=None, file_ext=".geojson", **kwargs: Any
):
    """
    Converts all vector files in a directory to GeoPackage format in batch.

    Args:
        input_dir (str): The directory containing the input vector files.
        output_dir (str, optional): The directory to save the converted GeoPackage files.
            If not provided, the input directory will be used. Defaults to None.
        file_ext (str): The file extension of the input vector files (e.g., ".geojson"). Defaults to ".geojson".
        **kwargs: Additional keyword arguments to be passed to the glob.glob function for file matching.

    Returns:
        None

    Example:
        >>> vector_to_gpkg_batch("input_directory", "output_directory", ".geojson")
    """
    import glob

    import duckdb

    # Set output directory
    if output_dir is None:
        output_dir = input_dir

    # Create output directory if it doesn't exist
    os.makedirs(output_dir, exist_ok=True)

    # Initialize DuckDB connection
    conn = duckdb.connect(database=":memory:")

    # Install and load required extensions
    conn.execute("INSTALL spatial")
    conn.execute("LOAD spatial")

    # Get all GeoJSON files in the input directory
    files = glob.glob(os.path.join(input_dir, f"*.{file_ext.lstrip('.')}"), **kwargs)

    # Process each file
    for index, file in enumerate(files):
        # Get base filename without extension
        base_name = os.path.basename(file)
        file_name_without_ext = os.path.splitext(base_name)[0]

        # Define output path
        gpkg_file = os.path.join(output_dir, f"{file_name_without_ext}.gpkg")

        print(f"Converting {index + 1}/{len(files)}: {base_name} to GeoPackage...")

        try:
            # Execute the conversion
            conn.execute(
                f"""
                COPY (
                    SELECT * FROM ST_Read('{file}')
                ) TO '{gpkg_file}' (FORMAT GDAL, DRIVER 'GPKG')
            """
            )
        except Exception as e:
            print(f"Error converting {base_name}: {str(e)}")

    # Close connection
    conn.close()
    print("All conversions complete!")


def vector_to_geojson_batch(input_dir, output_dir=None, file_ext=".shp", **kwargs: Any):
    """
    Converts all vector files in a directory to GeoJSON format in batch.

    Args:
        input_dir (str): The directory containing the input vector files.
        output_dir (str, optional): The directory to save the converted GeoPackage files.
            If not provided, the input directory will be used. Defaults to None.
        file_ext (str): The file extension of the input vector files (e.g., ".shp"). Defaults to ".shp".
        **kwargs: Additional keyword arguments to be passed to the glob.glob function for file matching.

    Returns:
        None

    Example:
        >>> vector_to_geojson_batch("input_directory", "output_directory", ".shp")
    """
    import glob

    import duckdb

    # Set output directory
    if output_dir is None:
        output_dir = input_dir

    # Create output directory if it doesn't exist
    os.makedirs(output_dir, exist_ok=True)

    # Initialize DuckDB connection
    conn = duckdb.connect(database=":memory:")

    # Install and load required extensions
    conn.execute("INSTALL spatial")
    conn.execute("LOAD spatial")

    # Get all GeoJSON files in the input directory
    files = glob.glob(os.path.join(input_dir, f"*.{file_ext.lstrip('.')}"), **kwargs)

    # Process each file
    for index, file in enumerate(files):
        # Get base filename without extension
        base_name = os.path.basename(file)
        file_name_without_ext = os.path.splitext(base_name)[0]

        # Define output path
        gpkg_file = os.path.join(output_dir, f"{file_name_without_ext}.geojson")

        print(f"Converting {index + 1}/{len(files)}: {base_name} to GeoJSON...")

        try:
            # Execute the conversion
            conn.execute(
                f"""
                COPY (
                    SELECT * FROM ST_Read('{file}')
                ) TO '{gpkg_file}' (FORMAT GDAL, DRIVER 'GeoJSON')
            """
            )
        except Exception as e:
            print(f"Error converting {base_name}: {str(e)}")

    # Close connection
    conn.close()
    print("All conversions complete!")


def geojsonl_to_parquet_batch(
    input_dir,
    output_dir,
    batch_size=50,
    file_ext=".json",
    filename_predix="batch_",
    **kwargs: Any,
):
    """
    Convert JSON Lines files to multiple GeoParquet files, with each GeoParquet file
    containing data from a specified number of JSON Lines files.

    Args:
        input_dir (str): Directory containing JSON Lines files to convert
        output_dir (str): Directory for output GeoParquet files
        batch_size (int, optional): Number of JSON Lines files to combine in each GeoParquet file.
                                    Defaults to 50.
        file_ext (str, optional): File extension of the input files. Defaults to ".json".
        filename_predix (str, optional): Prefix for the output GeoParquet files. Defaults to "batch_".
        **kwargs: Additional keyword arguments to pass to the `to_parquet` function of GeoDataFrame.

    """
    import glob
    import math

    import geopandas as gpd
    from shapely.geometry import shape

    if not os.path.exists(input_dir):
        raise FileNotFoundError(f"Input directory not found: {input_dir}")

    # Get all JSON files
    json_files = glob.glob(os.path.join(input_dir, f"*.{file_ext.lstrip('.')}"))

    if not json_files:
        raise ValueError(f"No JSON files found in {input_dir}")

    # Create output directory if it doesn't exist
    os.makedirs(output_dir, exist_ok=True)

    # Calculate number of output files
    num_files = len(json_files)
    num_batches = math.ceil(num_files / batch_size)

    print(
        f"Processing {num_files} JSON Lines files into {num_batches} GeoParquet files"
    )

    # Track statistics
    processed_files = 0
    processed_records = 0
    failed_files = 0
    successful_parquets = 0

    # Process files in batches
    for batch_num in range(num_batches):
        print(f"\nProcessing batch {batch_num+1}/{num_batches}")

        # Generate output filename
        output_file = os.path.join(
            output_dir, f"{filename_predix}{batch_num+1:04d}.parquet"
        )
        if os.path.exists(output_file):
            print(f"Output file already exists: {output_file}")
            continue

        start_idx = batch_num * batch_size
        end_idx = min(start_idx + batch_size, num_files)
        batch_files = json_files[start_idx:end_idx]

        records = []

        for file_path in batch_files:
            try:
                file_records = 0

                # Process the file line by line (JSON Lines format)
                with open(file_path, "r", encoding="utf-8") as f:
                    for line in f:
                        if line.strip():  # Skip empty lines
                            try:
                                data = json.loads(line)

                                # Create a record with properties and geometry
                                record = {}

                                # Extract all properties except geometry
                                for key, value in data.items():
                                    if key != "geometry":
                                        record[key] = value

                                # Handle geometry
                                if "geometry" in data:
                                    # Convert the geometry to a Shapely object
                                    record["geometry"] = shape(data["geometry"])
                                else:
                                    continue  # Skip records without geometry

                                records.append(record)
                                file_records += 1
                            except json.JSONDecodeError as e:
                                print(
                                    f"Error decoding JSON in file {file_path}: {str(e)}"
                                )
                                continue  # Skip invalid JSON lines
                            except Exception as e:
                                print(
                                    f"Error processing record in file {file_path}: {str(e)}"
                                )
                                continue  # Skip problematic records

                # print(f"Processed {file_records} records from {file_path}")
                processed_records += file_records
                processed_files += 1

            except Exception as e:
                print(f"Error processing file {file_path}: {str(e)}")
                failed_files += 1

        if not records:
            print(f"No valid records found in batch {batch_num+1}")
            continue

        # Create a GeoDataFrame with the correct CRS
        gdf = gpd.GeoDataFrame(records, geometry="geometry", crs="EPSG:4326")

        # Write to GeoParquet
        gdf.to_parquet(output_file, index=False, **kwargs)
        successful_parquets += 1

        # Print summary for this batch
        print(f"Created GeoParquet file {batch_num+1}/{num_batches}: {output_file}")
        print(f"  - Number of features: {len(gdf)}")
        print(f"  - Columns: {list(gdf.columns)}")
        if len(gdf) > 0:
            print(
                f"  - First record attributes: {[k for k in gdf.iloc[0].keys() if k != 'geometry']}"
            )

    # Print final summary
    print(f"\nSummary:")
    print(f"Total files processed: {processed_files} of {num_files}")
    print(f"Failed files: {failed_files}")
    print(f"Total records processed: {processed_records}")
    print(f"GeoParquet files created: {successful_parquets}")


def extract_parquet_by_bbox(
    input_parquet, bbox, output_file, geometry="geometry", driver="PARQUET"
) -> None:
    """
    Extract buildings that intersect with a specific bounding box in San Diego.

    Uses DuckDB with spatial extension to query buildings that intersect with
    a bounding box and saves the results to a Parquet file.

    Args:
        input_parquet (str): Path to the input Parquet file.
        bbox (tuple | list): Bounding box as (minx, miny, maxx, maxy).
        output_file (str): Output file path for resulting Parquet/GeoParquet file.
        geometry (str, optional): Geometry column name. Defaults to "geometry".
        driver (str, optional): Output driver, e.g., "PARQUET". Defaults to "PARQUET".

    Returns:
        The function writes the results to the output file.
    """
    import duckdb

    # Connect to DuckDB
    conn = duckdb.connect()

    # Install and load spatial extension
    conn.execute("INSTALL spatial")
    conn.execute("LOAD spatial")

    if driver.upper() == "PARQUET":
        fmt = "FORMAT PARQUET"
    else:
        fmt = f"FORMAT GDAL, DRIVER '{driver}'"

    # Run the query
    query = f"""
    COPY (
        WITH bbox AS (
            SELECT ST_MakeEnvelope({bbox[0]}, {bbox[1]}, {bbox[2]}, {bbox[3]}) AS geom2
        )

        SELECT * FROM '{input_parquet}'
        WHERE
            ST_Intersects(
                {geometry},
                (SELECT geom2 FROM bbox)
            )
    ) TO '{output_file}' ({fmt})
    """

    # Execute the query
    conn.execute(query)

    # Close the connection
    conn.close()


def get_vector_column_names(input_vector, db_con=None):
    """
    Retrieves the column names from a DuckDB table.

    Args:
        input_vector (str): The path to the input vector file (e.g., a Parquet or GeoPackage file).
        db_con (duckdb.Connection, optional): An existing DuckDB connection. If None, a new connection will be created.

    Returns:
        A list of column names from the specified table.

    Raises:
        duckdb.CatalogException: If the table does not exist.
    """
    import duckdb

    if db_con is None:
        db_con = duckdb.connect()

    db_con.execute("INSTALL spatial;")
    db_con.execute("LOAD spatial;")

    if not os.path.exists(input_vector):
        raise ValueError(f"Input vector file does not exist: {input_vector}")

    if input_vector.endswith(".parquet"):
        query = f"SELECT * FROM '{input_vector}' LIMIT 0"
    else:
        query = f"SELECT * FROM ST_Read('{input_vector}') LIMIT 0"
    db_con.execute(query)
    return [desc[0] for desc in db_con.description]


def get_parquet_geometry_column(input_parquet: str, db_con=None) -> str:
    """
    Retrieves the geometry column name from a Parquet file.

    This function checks for the presence of a geometry column in the input Parquet file.
    It looks for columns named "geometry" or "geom" and returns the first match.

    Args:
        input_parquet (str): The path to the input Parquet file.
        db_con (duckdb.Connection, optional): An existing DuckDB connection. If None, a new connection will be created.

    Returns:
        The name of the geometry column ("geometry" or "geom").

    Raises:
        ValueError: If no recognized geometry column is found in the input Parquet file.

    Example:
        >>> geometry_column = get_parquet_geometry_column("data.parquet")
        >>> print(geometry_column)
        "geometry"
    """
    column_names = get_vector_column_names(input_parquet, db_con=db_con)
    if "geometry" in column_names:
        return "geometry"
    elif "geom" in column_names:
        return "geom"
    else:
        raise ValueError(
            f"The input vector file does not contain a recognized geometry column. "
            f"Available columns: {column_names}. Please ensure the vector file has a 'geometry' or 'geom' column."
        )


def get_vector_metadata(input_vector, db_con=None):
    """
    Retrieves metadata for a vector file.

    This function uses DuckDB with the spatial extension to extract metadata
    about the layers in the input vector file.

    Args:
        input_vector (str): The path to the input vector file.
        db_con (duckdb.Connection, optional): An existing DuckDB connection. If None, a new connection will be created.

    Returns:
        A dictionary containing metadata about the vector file.

    Raises:
        ValueError: If the input vector file does not exist.

    Example:
        >>> metadata = get_vector_metadata("data.gpkg")
        >>> print(metadata)
        {'geometry_fields': [{'name': 'geom', 'crs': {'auth_name': 'EPSG', 'auth_code': '4326'}}], ...}
    """
    import duckdb

    if db_con is None:
        db_con = duckdb.connect()

    db_con.execute("INSTALL spatial;")
    db_con.execute("LOAD spatial;")

    if not os.path.exists(input_vector):
        raise ValueError(f"Input vector file does not exist: {input_vector}")

    query = f"SELECT * FROM ST_Read_Meta('{input_vector}')"
    df = db_con.execute(query).fetch_df()
    meta = df["layers"][0][0]
    return meta


def get_vector_crs(input_vector, db_con=None, return_epsg=False):
    """
    Retrieves the Coordinate Reference System (CRS) of a vector file.

    This function extracts the CRS information from the metadata of the input vector file.

    Args:
        input_vector (str): The path to the input vector file.
        db_con (duckdb.Connection, optional): An existing DuckDB connection. If None, a new connection will be created.
        return_epsg (bool): Whether to return the EPSG code of the CRS. Defaults to False.

    Returns:
        Union[dict, int]: The CRS information as a dictionary or the EPSG code as an integer.

    Raises:
        ValueError: If the CRS information is not available in the input vector file.

    Example:
        >>> crs = get_vector_crs("data.gpkg", return_epsg=True)
        >>> print(crs)
        4326
    """
    metadata = get_vector_metadata(input_vector, db_con=db_con)
    crs = metadata["geometry_fields"][0]["crs"]
    if return_epsg:
        if crs["auth_name"] == "EPSG" and len(crs["auth_code"]) > 0:
            return int(crs["auth_code"])  # Return the EPSG code if available
        else:
            raise ValueError(
                f"CRS information is not available in the input vector file: {input_vector}. "
            )
    else:
        return crs


def split_parquet_by_geometries(
    input_parquet,
    output_dir,
    input_vector,
    column,
    filename_prefix="",
    filename_suffix="",
    driver="PARQUET",
    verbose=True,
    **kwargs,
):
    """
    Split a Parquet file containing geometries based on intersection with features from a vector file.

    Args:
        input_parquet (str): Path to the input Parquet file containing geometries.
        output_dir (str): Directory to save the split files.
        input_vector (str): Path to the vector file containing geometries to split by.
        column (str): Column name in the vector file to use for splitting.
        filename_prefix (str): Prefix for output filenames.
        filename_suffix (str): Suffix for output filenames.
        driver (str): Output format driver (e.g., "PARQUET", "GPKG").
        verbose (bool): Whether to print progress information.
        **kwargs: Additional arguments.

    Raises:
        ParserException: If there's a syntax error in the SQL query.
    """
    import duckdb

    # Connect to DuckDB
    con = duckdb.connect()

    # Load spatial extension
    con.execute("INSTALL spatial;")
    con.execute("LOAD spatial;")

    if driver.upper() == "PARQUET":
        fmt = "FORMAT PARQUET"
    else:
        fmt = f"FORMAT GDAL, DRIVER '{driver}'"

    os.makedirs(output_dir, exist_ok=True)

    if input_vector.endswith(".parquet"):
        read_str = f"'{input_vector}'"
    else:
        read_str = f"ST_Read('{input_vector}')"

    # Get all state IDs from the parquet file
    state_ids = con.execute(
        f"""
        SELECT {column} FROM {read_str}
        """
    ).fetchall()

    state_ids.sort()

    geometry = "geometry"

    column_names = get_vector_column_names(input_vector, db_con=con)
    if "geometry" not in column_names:
        if "geom" in column_names:
            geometry = "geom"  # Fallback to geom if geometry is not available
        else:
            raise ValueError(
                f"The input vector file does not contain a geometry column. Available columns: {column_names}"
            )

    # Loop through each state and save buildings
    for index, (state_id,) in enumerate(state_ids):
        if verbose:
            print(
                f"Processing {index + 1}/{len(state_ids)}: Extracting geometries for '{state_id}'..."
            )

        output_path = os.path.join(
            output_dir, f"{filename_prefix}{state_id}{filename_suffix}.{driver.lower()}"
        )

        # Use proper path formatting
        query = f"""
        COPY (
            WITH bbox AS (
                SELECT {geometry} AS geom2
                FROM {read_str}
                WHERE {column} = '{state_id}'
            )
            SELECT * FROM '{input_parquet}'
            WHERE ST_Intersects({geometry}, (SELECT geom2 FROM bbox))
        ) TO '{output_path}' ({fmt});
        """

        con.execute(query)

    con.close()
    print("Done!")


def parquet_to_gdf(
    input_parquet,
    db_con=None,
    src_crs="EPSG:4326",
    dst_crs="EPSG:4326",
    columns=None,
    limit=None,
    **kwargs: Any,
):

    import duckdb

    if db_con is None:
        db_con = duckdb.connect()

    db_con.execute("INSTALL spatial;")
    db_con.execute("LOAD spatial;")

    geometry = get_parquet_geometry_column(input_parquet, db_con=db_con)
    geom_sql = f"ST_AsText(ST_GeomFromWKB(ST_AsWKB({geometry}))) AS {geometry}"

    if columns is None:
        columns = "*"

    if isinstance(columns, list):
        # Join the columns into a string for SQL query (quote to handle dots in names)
        columns = ", ".join([f'"{col}"' for col in columns])
        sql = f"SELECT {columns}, {geom_sql} FROM '{input_parquet}'"
    else:

        sql = f"SELECT {columns} EXCLUDE {geometry}, {geom_sql} FROM '{input_parquet}'"
    if limit is not None:
        sql += f" LIMIT {limit}"

    df = db_con.sql(sql).df()
    gdf = df_to_gdf(df, geometry=geometry, src_crs=src_crs, dst_crs=dst_crs, **kwargs)
    db_con.close()
    return gdf


def df_to_gdf(
    df, geometry="geometry", src_crs="EPSG:4326", dst_crs=None, **kwargs: Any
) -> Any:
    """
    Converts a pandas DataFrame to a GeoPandas GeoDataFrame.

    Args:
        df (pandas.DataFrame): The pandas DataFrame to convert.
        geometry (str): The name of the geometry column in the DataFrame.
        src_crs (str): The coordinate reference system (CRS) of the GeoDataFrame. Default is "EPSG:4326".
        dst_crs (str): The target CRS of the GeoDataFrame. Default is None

    Returns:
        The converted GeoPandas GeoDataFrame.
    """
    import geopandas as gpd
    from shapely import wkt

    # Convert the geometry column to Shapely geometry objects
    df[geometry] = df[geometry].apply(lambda x: wkt.loads(x))

    # Convert the pandas DataFrame to a GeoPandas GeoDataFrame
    gdf = gpd.GeoDataFrame(df, geometry=geometry, crs=src_crs, **kwargs)
    if dst_crs is not None and dst_crs != src_crs:
        gdf = gdf.to_crs(dst_crs)

    return gdf


def check_url(url: str) -> bool:
    """Check if an HTTP URL is working.

    Args:
        url (str): The URL to check.

    Returns:
        True if the URL is working (returns a 200 status code), False otherwise.
    """
    try:
        response = requests.get(url)
        if response.status_code == 200:
            return True
        else:
            return False
    except requests.exceptions.RequestException:
        return False


def read_parquet(
    source: str,
    geometry: Optional[str] = None,
    columns: Optional[Union[str, list]] = None,
    exclude: Optional[Union[str, list]] = None,
    db: Optional[str] = None,
    table_name: Optional[str] = None,
    sql: Optional[str] = None,
    limit: Optional[int] = None,
    src_crs: Optional[str] = None,
    dst_crs: Optional[str] = None,
    return_type: str = "gdf",
    **kwargs: Any,
):
    """
    Read Parquet data from a source and return a GeoDataFrame or DataFrame.

    Args:
        source (str): The path to the Parquet file or directory containing Parquet files.
        geometry (str, optional): The name of the geometry column. Defaults to None.
        columns (str or list, optional): The columns to select. Defaults to None (select all columns).
        exclude (str or list, optional): The columns to exclude from the selection. Defaults to None.
        db (str, optional): The DuckDB database path or alias. Defaults to None.
        table_name (str, optional): The name of the table in the DuckDB database. Defaults to None.
        sql (str, optional): The SQL query to execute. Defaults to None.
        limit (int, optional): The maximum number of rows to return. Defaults to None (return all rows).
        src_crs (str, optional): The source CRS (Coordinate Reference System) of the geometries. Defaults to None.
        dst_crs (str, optional): The target CRS to reproject the geometries. Defaults to None.
        return_type (str, optional): The type of object to return:
            - 'gdf': GeoDataFrame (default)
            - 'df': DataFrame
            - 'numpy': NumPy array
            - 'arrow': Arrow Table
            - 'polars': Polars DataFrame
        **kwargs: Additional keyword arguments that are passed to the DuckDB connection.

    Returns:
        Union[gpd.GeoDataFrame, pd.DataFrame, np.ndarray]: The loaded data.

    Raises:
        ValueError: If the columns or exclude arguments are not of the correct type.

    """
    import duckdb

    if isinstance(db, str):
        con = duckdb.connect(db)
    else:
        con = duckdb.connect()

    con.install_extension("httpfs")
    con.load_extension("httpfs")

    con.install_extension("spatial")
    con.load_extension("spatial")

    if columns is None:
        columns = "*"
    elif isinstance(columns, list):
        # Quote column names to handle dots in names (e.g., "names.primary")
        columns = ", ".join([f'"{col}"' for col in columns])
    elif not isinstance(columns, str):
        raise ValueError("columns must be a list or a string.")

    if exclude is not None:
        if isinstance(exclude, list):
            # Quote column names in exclude list too
            exclude = ", ".join([f'"{col}"' for col in exclude])
        elif not isinstance(exclude, str):
            raise ValueError("exclude_columns must be a list or a string.")
        columns = f"{columns} EXCLUDE {exclude}"

    result = None
    if return_type in ["df", "numpy", "arrow", "polars"]:
        if sql is None:
            sql = f"SELECT {columns} FROM '{source}'"
        if limit is not None:
            sql += f" LIMIT {limit}"

        if return_type == "df":
            result = con.sql(sql, **kwargs).df()
        elif return_type == "numpy":
            result = con.sql(sql, **kwargs).fetchnumpy()
        elif return_type == "arrow":
            result = con.sql(sql, **kwargs).arrow()
        elif return_type == "polars":
            result = con.sql(sql, **kwargs).pl()

        if table_name is not None:
            con.sql(f"CREATE OR REPLACE TABLE {table_name} AS FROM result", **kwargs)

    elif return_type == "gdf":
        if geometry is None:
            geometry = "geometry"
        if sql is None:
            # if src_crs is not None and dst_crs is not None:
            #     geom_sql = f"ST_AsText(ST_Transform(ST_GeomFromWKB({geometry}), '{src_crs}', '{dst_crs}', true)) AS {geometry}"
            # else:
            geom_sql = f"ST_AsText(ST_GeomFromWKB(ST_AsWKB({geometry}))) AS {geometry}"
            sql = f"SELECT {columns} EXCLUDE {geometry}, {geom_sql} FROM '{source}'"
        if limit is not None:
            sql += f" LIMIT {limit}"

        df = con.sql(sql, **kwargs).df()
        if table_name is not None:
            con.sql(f"CREATE OR REPLACE TABLE {table_name} AS FROM df", **kwargs)
        result = df_to_gdf(df, geometry=geometry, src_crs=src_crs, dst_crs=dst_crs)

    con.close()
    return result


def assign_discrete_colors(
    df, column, cmap, to_rgb=True, return_type="array"
) -> Union[List, "np.ndarray"]:
    """
    Assigns unique colors to each category in a categorical column of a dataframe.

    Args:
        df (pandas.DataFrame): The input dataframe.
        column (str): The name of the categorical column.
        cmap (dict): A dictionary mapping categories to colors.
        to_rgb (bool): Whether to convert the colors to RGB values. Defaults to True.
        return_type (str): The type of the returned values. Can be 'list' or 'array'. Defaults to 'array'.

    Returns:
        A list of colors for each category in the categorical column.
    """
    import numpy as np

    # Copy the categorical column from the original dataframe
    category_column = df[column].copy()

    # Map colors to the categorical values
    category_column = category_column.map(cmap)

    values = category_column.values.tolist()

    if to_rgb:
        values = [hex_to_rgb(check_color(color)) for color in values]
        if return_type == "array":
            values = np.array(values, dtype=np.uint8)

    return values


def assign_continuous_colors(
    df,
    column: str,
    cmap: str = None,
    colors: list = None,
    labels: list = None,
    scheme: str = "Quantiles",
    k: int = 5,
    legend_kwds: dict = None,
    classification_kwds: dict = None,
    to_rgb: bool = True,
    return_type: str = "array",
    return_legend: bool = False,
) -> Union[np.ndarray, Tuple[np.ndarray, dict]]:
    """Assigns continuous colors to a DataFrame column based on a specified scheme.

    Args:
        df (pd.DataFrame): A pandas DataFrame.
        column: The name of the column to assign colors.
        cmap: The name of the colormap to use.
        colors: A list of custom colors.
        labels: A list of custom labels for the legend.
        scheme: The scheme for classifying the data. Default is 'Quantiles'.
        k: The number of classes for classification.
        legend_kwds: Additional keyword arguments for configuring the legend.
        classification_kwds: Additional keyword arguments for configuring the classification.
        to_rgb: Whether to convert colors to RGB values. Default is True.
        return_type: The type of the returned values. Default is 'array'.
        return_legend: Whether to return the legend. Default is False.

    Returns:
        The assigned colors as a numpy array or a tuple containing the colors and the legend, depending on the value of return_legend.
    """
    import numpy as np

    data = df[[column]].copy()
    new_df, legend = classify(
        data, column, cmap, colors, labels, scheme, k, legend_kwds, classification_kwds
    )
    values = new_df["color"].values.tolist()

    if to_rgb:
        values = [hex_to_rgb(check_color(color)) for color in values]
        if return_type == "array":
            values = np.array(values, dtype=np.uint8)

    if return_legend:
        return values, legend
    else:
        return values


def gedi_search(
    roi: Any,
    start_date: Optional[str] = None,
    end_date: Optional[str] = None,
    add_roi: bool = False,
    return_type: str = "gdf",
    output: Optional[str] = None,
    sort_filesize: bool = False,
    **kwargs: Any,
) -> Union[pd.DataFrame, None]:
    """
    Searches for GEDI data using the Common Metadata Repository (CMR) API.
    The source code for this function is adapted from https://github.com/ornldaac/gedi_tutorials.
    Credits to ORNL DAAC and Rupesh Shrestha.

    Args:
        roi: A list, tuple, or file path representing the bounding box coordinates
            in the format (min_lon, min_lat, max_lon, max_lat), or a GeoDataFrame
            containing the region of interest geometry.
        start_date: The start date of the temporal range to search for data
            in the format 'YYYY-MM-DD'.
        end_date: The end date of the temporal range to search for data
            in the format 'YYYY-MM-DD'.
        add_roi: A boolean value indicating whether to include the region of interest
            as a granule in the search results. Default is False.
        return_type: The type of the search results to return. Must be one of 'df'
            (DataFrame), 'gdf' (GeoDataFrame), or 'csv' (CSV file). Default is 'gdf'.
        output: The file path to save the CSV output when return_type is 'csv'.
            Optional and only applicable when return_type is 'csv'.
        sort_filesize: A boolean value indicating whether to sort the search results.
        **kwargs: Additional keyword arguments to be passed to the CMR API.

    Returns:
        The search results as a pandas DataFrame (return_type='df'), geopandas GeoDataFrame
        (return_type='gdf'), or a CSV file (return_type='csv').

    Raises:
        ValueError: If roi is not a list, tuple, or file path.

    """

    import datetime as dt

    import geopandas as gpd
    import pandas as pd
    import requests
    from shapely.geometry import MultiPolygon, Polygon, box
    from shapely.ops import orient

    # CMR API base url
    cmrurl = "https://cmr.earthdata.nasa.gov/search/"

    doi = "10.3334/ORNLDAAC/2056"  # GEDI L4A DOI

    # Construct the DOI search URL
    doisearch = cmrurl + "collections.json?doi=" + doi

    # Send a request to the CMR API to get the concept ID
    response = requests.get(doisearch)
    response.raise_for_status()
    concept_id = response.json()["feed"]["entry"][0]["id"]

    # CMR formatted start and end times
    if start_date is not None and end_date is not None:
        dt_format = "%Y-%m-%dT%H:%M:%SZ"
        start_date = dt.datetime.strptime(start_date, "%Y-%m-%d")
        end_date = dt.datetime.strptime(end_date, "%Y-%m-%d")
        temporal_str = (
            start_date.strftime(dt_format) + "," + end_date.strftime(dt_format)
        )
    else:
        temporal_str = None

    # CMR formatted bounding box
    if isinstance(roi, list) or isinstance(roi, tuple):
        bound_str = ",".join(map(str, roi))
    elif isinstance(roi, str):
        roi = gpd.read_file(roi)
        roi.geometry = roi.geometry.apply(orient, args=(1,))  # make counter-clockwise
    elif isinstance(roi, gpd.GeoDataFrame):
        roi.geometry = roi.geometry.apply(orient, args=(1,))  # make counter-clockwise
    else:
        raise ValueError("roi must be a list, tuple, or a file path.")

    page_num = 1
    page_size = 2000  # CMR page size limit

    granule_arr = []

    while True:
        # Define CMR search parameters
        cmr_param = {
            "collection_concept_id": concept_id,
            "page_size": page_size,
            "page_num": page_num,
        }

        if temporal_str is not None:
            cmr_param["temporal"] = temporal_str

        if kwargs:
            cmr_param.update(kwargs)

        granulesearch = cmrurl + "granules.json"

        if isinstance(roi, list) or isinstance(roi, tuple):
            cmr_param["bounding_box[]"] = bound_str
            response = requests.get(granulesearch, params=cmr_param)
            response.raise_for_status()
        else:
            cmr_param["simplify-shapefile"] = "true"
            geojson = {
                "shapefile": (
                    "region.geojson",
                    roi.geometry.to_json(),
                    "application/geo+json",
                )
            }
            response = requests.post(granulesearch, data=cmr_param, files=geojson)

        # Send a request to the CMR API to get the granules
        granules = response.json()["feed"]["entry"]

        if granules:
            for index, g in enumerate(granules):
                granule_url = ""
                granule_poly = ""

                # Read file size
                granule_size = float(g["granule_size"])

                # Read bounding geometries
                if "polygons" in g:
                    polygons = g["polygons"]
                    multipolygons = []
                    for poly in polygons:
                        i = iter(poly[0].split(" "))
                        ltln = list(map(" ".join, zip(i, i)))
                        multipolygons.append(
                            Polygon(
                                [
                                    [float(p.split(" ")[1]), float(p.split(" ")[0])]
                                    for p in ltln
                                ]
                            )
                        )
                    granule_poly = MultiPolygon(multipolygons)

                # Get URL to HDF5 files
                for links in g["links"]:
                    if (
                        "title" in links
                        and links["title"].startswith("Download")
                        and links["title"].endswith(".h5")
                    ):
                        granule_url = links["href"]

                granule_id = g["id"]
                title = g["title"]
                time_start = g["time_start"]
                time_end = g["time_end"]

                granule_arr.append(
                    [
                        granule_id,
                        title,
                        time_start,
                        time_end,
                        granule_size,
                        granule_url,
                        granule_poly,
                    ]
                )

            page_num += 1
        else:
            break

    # Add bound as the last row into the dataframe
    if add_roi:
        if isinstance(roi, list) or isinstance(roi, tuple):
            b = list(roi)
            granule_arr.append(
                ["roi", None, None, None, 0, None, box(b[0], b[1], b[2], b[3])]
            )
        else:
            granule_arr.append(["roi", None, None, None, 0, None, roi.geometry.item()])

    # Create a pandas dataframe
    columns = [
        "id",
        "title",
        "time_start",
        "time_end",
        "granule_size",
        "granule_url",
        "granule_poly",
    ]
    l4adf = pd.DataFrame(granule_arr, columns=columns)

    # Drop granules with empty geometry
    l4adf = l4adf[l4adf["granule_poly"] != ""]

    if sort_filesize:
        l4adf = l4adf.sort_values(by=["granule_size"], ascending=True)

    if return_type == "df":
        return l4adf
    elif return_type == "gdf":
        gdf = gpd.GeoDataFrame(l4adf, geometry="granule_poly")
        gdf.crs = "EPSG:4326"
        return gdf
    elif return_type == "csv":
        columns.remove("granule_poly")
        return l4adf.to_csv(output, index=False, columns=columns)
    else:
        raise ValueError("return_type must be one of 'df', 'gdf', or 'csv'.")


def gedi_subset(
    spatial=None,
    start_date=None,
    end_date=None,
    out_dir=None,
    collection=None,
    variables=["all"],
    max_results=None,
    username=None,
    password=None,
    overwrite=False,
    **kwargs: Any,
):
    """
    Subsets GEDI data using the Harmony API.

    Args:
        spatial (Union[str, gpd.GeoDataFrame, List[float]], optional): Spatial extent for subsetting.
            Can be a file path to a shapefile, a GeoDataFrame, or a list of bounding box coordinates [minx, miny, maxx, maxy].
            Defaults to None.
        start_date (str, optional): Start date for subsetting in 'YYYY-MM-DD' format.
            Defaults to None.
        end_date (str, optional): End date for subsetting in 'YYYY-MM-DD' format.
            Defaults to None.
        out_dir (str, optional): Output directory to save the subsetted files.
            Defaults to None, which will use the current working directory.
        collection (Collection, optional): GEDI data collection. If not provided,
            the default collection with DOI '10.3334/ORNLDAAC/2056' will be used.
            Defaults to None.
        variables (List[str], optional): List of variable names to subset.
            Defaults to ['all'], which subsets all available variables.
        max_results (int, optional): Maximum number of results to return.
            Defaults to None, which returns all results.
        username (str, optional): Earthdata username.
            Defaults to None, which will attempt to read from the 'EARTHDATA_USERNAME' environment variable.
        password (str, optional): Earthdata password.
            Defaults to None, which will attempt to read from the 'EARTHDATA_PASSWORD' environment variable.
        overwrite (bool, optional): Whether to overwrite existing files in the output directory.
            Defaults to False.
        **kwargs: Additional keyword arguments to pass to the Harmony API request.

    Raises:
        ImportError: If the 'harmony' package is not installed.
        ValueError: If the 'spatial', 'start_date', or 'end_date' arguments are not valid.

    Returns:
        None.
    """

    try:
        import harmony  # pylint: disable=E0401
    except ImportError:
        install_package("harmony-py")

    from datetime import datetime

    import geopandas as gpd
    import requests as re
    from harmony import (
        BBox,
        Client,
        Collection,  # pylint: disable=E0401
        Environment,
        Request,
    )

    if out_dir is None:
        out_dir = os.getcwd()

    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    if collection is None:
        # GEDI L4A DOI
        doi = "10.3334/ORNLDAAC/2056"

        # CMR API base url
        doisearch = f"https://cmr.earthdata.nasa.gov/search/collections.json?doi={doi}"
        concept_id = re.get(doisearch).json()["feed"]["entry"][0]["id"]
        concept_id
        collection = Collection(id=concept_id)

    if username is None:
        username = os.environ.get("EARTHDATA_USERNAME", None)
    if password is None:
        password = os.environ.get("EARTHDATA_PASSWORD", None)

    if username is None or password is None:
        raise ValueError("username and password must be provided.")

    harmony_client = Client(auth=(username, password))

    if isinstance(spatial, str):
        spatial = gpd.read_file(spatial)

    if isinstance(spatial, gpd.GeoDataFrame):
        spatial = spatial.total_bounds.tolist()

    if isinstance(spatial, list) and len(spatial) == 4:
        bounding_box = BBox(spatial[0], spatial[1], spatial[2], spatial[3])
    else:
        raise ValueError(
            "spatial must be a list of bounding box coordinates or a GeoDataFrame, or a file path."
        )

    if isinstance(start_date, str):
        start_date = datetime.strptime(start_date, "%Y-%m-%d")

    if isinstance(end_date, str):
        end_date = datetime.strptime(end_date, "%Y-%m-%d")

    if start_date is None or end_date is None:
        print("start_date and end_date must be provided.")
        temporal_range = None
    else:
        temporal_range = {"start": start_date, "end": end_date}

    request = Request(
        collection=collection,
        variables=variables,
        temporal=temporal_range,
        spatial=bounding_box,
        ignore_errors=True,
        max_results=max_results,
        **kwargs,
    )

    # submit harmony request, will return job id
    subset_job_id = harmony_client.submit(request)

    print(f"Processing job: {subset_job_id}")

    print(f"Waiting for the job to finish")
    results = harmony_client.result_json(subset_job_id, show_progress=True)

    print(f"Downloading subset files...")
    futures = harmony_client.download_all(
        subset_job_id, directory=out_dir, overwrite=overwrite
    )
    for f in futures:
        # all subsetted files have this suffix
        if f.result().endswith("subsetted.h5"):
            print(f"Downloaded: {f.result()}")

    print(f"Done downloading files.")


def gedi_download_file(
    url: str, filename: str = None, username: str = None, password: str = None
) -> None:
    """
    Downloads a file from the given URL and saves it to the specified filename.
    If no filename is provided, the name of the file from the URL will be used.

    Args:
        url (str): The URL of the file to download.
            e.g., https://daac.ornl.gov/daacdata/gedi/GEDI_L4A_AGB_Density_V2_1/data/GEDI04_A_2019298202754_O04921_01_T02899_02_002_02_V002.h5
        filename (str, optional): The name of the file to save the downloaded content to. Defaults to None.
        username (str, optional): Username for authentication. Can also be set using the EARTHDATA_USERNAME environment variable. Defaults to None.
            Create an account at https://urs.earthdata.nasa.gov
        password (str, optional): Password for authentication. Can also be set using the EARTHDATA_PASSWORD environment variable. Defaults to None.

    Returns:
        None
    """
    from urllib.parse import urlparse

    import requests
    from tqdm import tqdm

    if username is None:
        username = os.environ.get("EARTHDATA_USERNAME", None)
    if password is None:
        password = os.environ.get("EARTHDATA_PASSWORD", None)

    if username is None or password is None:
        raise ValueError(
            "Username and password must be provided. Create an account at https://urs.earthdata.nasa.gov."
        )

    with requests.Session() as session:
        r1 = session.request("get", url, stream=True)
        r = session.get(r1.url, auth=(username, password), stream=True)
        print(r.status_code)

        if r.status_code == 200:
            total_size = int(r.headers.get("content-length", 0))
            block_size = 1024  # 1 KB

            # Use the filename from the URL if not provided
            if not filename:
                parsed_url = urlparse(url)
                filename = parsed_url.path.split("/")[-1]

            progress_bar = tqdm(total=total_size, unit="B", unit_scale=True)

            with open(filename, "wb") as file:
                for data in r.iter_content(block_size):
                    progress_bar.update(len(data))
                    file.write(data)

            progress_bar.close()


def gedi_download_files(
    urls: List[str],
    outdir: str = None,
    filenames: str = None,
    username: str = None,
    password: str = None,
    overwrite: bool = False,
) -> None:
    """
    Downloads files from the given URLs and saves them to the specified directory.
    If no directory is provided, the current directory will be used.
    If no filenames are provided, the names of the files from the URLs will be used.

    Args:
        urls (List[str]): The URLs of the files to download.
            e.g., ["https://example.com/file1.txt", "https://example.com/file2.txt"]
        outdir (str, optional): The directory to save the downloaded files to. Defaults to None.
        filenames (str, optional): The names of the files to save the downloaded content to. Defaults to None.
        username (str, optional): Username for authentication. Can also be set using the EARTHDATA_USERNAME environment variable. Defaults to None.
            Create an account at https://urs.earthdata.nasa.gov
        password (str, optional): Password for authentication. Can also be set using the EARTHDATA_PASSWORD environment variable. Defaults to None.
        overwrite (bool): Whether to overwrite the existing output files. Default is False.

    Returns:
        None
    """

    from urllib.parse import urlparse

    import geopandas as gpd
    import requests
    from tqdm import tqdm

    if isinstance(urls, gpd.GeoDataFrame):
        urls = urls["granule_url"].tolist()

    session = requests.Session()

    if username is None:
        username = os.environ.get("EARTHDATA_USERNAME", None)
    if password is None:
        password = os.environ.get("EARTHDATA_PASSWORD", None)

    if username is None or password is None:
        print("Username and password must be provided.")
        return

    if outdir is None:
        outdir = os.getcwd()

    if not os.path.exists(outdir):
        os.makedirs(outdir)

    for index, url in enumerate(urls):
        print(f"Downloading file {index+1} of {len(urls)}...")

        if url is None:
            continue

        # Use the filename from the URL if not provided
        if not filenames:
            parsed_url = urlparse(url)
            filename = parsed_url.path.split("/")[-1]
        else:
            filename = filenames.pop(0)

        filepath = os.path.join(outdir, filename)
        if os.path.exists(filepath) and not overwrite:
            print(f"File {filepath} already exists. Skipping...")
            continue

        r1 = session.request("get", url, stream=True)
        r = session.get(r1.url, auth=(username, password), stream=True)

        if r.status_code == 200:
            total_size = int(r.headers.get("content-length", 0))
            block_size = 1024  # 1 KB

            progress_bar = tqdm(total=total_size, unit="B", unit_scale=True)

            with open(filepath, "wb") as file:
                for data in r.iter_content(block_size):
                    progress_bar.update(len(data))
                    file.write(data)

            progress_bar.close()

    session.close()


def h5_keys(filename: str) -> List[str]:
    """
    Retrieve the keys (dataset names) within an HDF5 file.

    Args:
        filename (str): The filename of the HDF5 file.

    Returns:
        A list of dataset names present in the HDF5 file.

    Raises:
        ImportError: Raised if h5py is not installed.

    Example:
        >>> keys = h5_keys('data.h5')
        >>> print(keys)
        [
    """
    try:
        import h5py
    except ImportError:
        raise ImportError(
            "h5py must be installed to use this function. Please install it with 'pip install h5py'."
        )

    with h5py.File(filename, "r") as f:
        keys = list(f.keys())

    return keys


def h5_variables(filename: str, key: str) -> List[str]:
    """
    Retrieve the variables (column names) within a specific key (dataset) in an H5 file.

    Args:
        filename (str): The filename of the H5 file.
        key (str): The key (dataset name) within the H5 file.

    Returns:
        A list of variable names (column names) within the specified key.

    Raises:
        ImportError: Raised if h5py is not installed.

    Example:
        >>> variables = h5_variables('data.h5', 'dataset1')
        >>> print(variables)
        ['var1', 'var2', 'var3']
    """
    try:
        import h5py
    except ImportError:
        raise ImportError(
            "h5py must be installed to use this function. Please install it with 'pip install h5py'."
        )

    with h5py.File(filename, "r") as f:
        cols = list(f[key].keys())

    return cols


def h5_to_gdf(
    filenames: str,
    dataset: str,
    lat: str = "lat_lowestmode",
    lon: str = "lon_lowestmode",
    columns: Optional[List[str]] = None,
    crs: str = "EPSG:4326",
    nodata=None,
    **kwargs: Any,
):
    """
    Read data from one or multiple HDF5 files and return as a GeoDataFrame.

    Args:
        filenames (str or List[str]): The filename(s) of the HDF5 file(s).
        dataset (str): The dataset name within the H5 file(s).
        lat (str): The column name representing latitude. Default is 'lat_lowestmode'.
        lon (str): The column name representing longitude. Default is 'lon_lowestmode'.
        columns (List[str], optional): List of column names to include. If None, all columns will be included. Default is None.
        crs (str, optional): The coordinate reference system code. Default is "EPSG:4326".
        **kwargs: Additional keyword arguments to be passed to the GeoDataFrame constructor.

    Returns:
        A GeoDataFrame containing the data from the H5 file(s).

    Raises:
        ImportError: Raised if h5py is not installed.
        ValueError: Raised if the provided filenames argument is not a valid type or if a specified file does not exist.

    Example:
        >>> gdf = h5_to_gdf('data.h5', 'dataset1', 'lat', 'lon', columns=['column1', 'column2'], crs='EPSG:4326')
        >>> print(gdf.head())
           column1  column2        lat        lon                    geometry
        0        10       20  40.123456 -75.987654  POINT (-75.987654 40.123456)
        1        15       25  40.234567 -75.876543  POINT (-75.876543 40.234567)
        ...

    """
    try:
        import h5py
    except ImportError:
        install_package("h5py")
        import h5py

    import glob

    import geopandas as gpd
    import pandas as pd

    if isinstance(filenames, str):
        if os.path.exists(filenames):
            files = [filenames]
        else:
            files = glob.glob(filenames)
            if not files:
                raise ValueError(f"File {filenames} does not exist.")
            files.sort()
    elif isinstance(filenames, list):
        files = filenames
    else:
        raise ValueError("h5_file must be a string or a list of strings.")

    out_df = pd.DataFrame()

    for file in files:
        h5 = h5py.File(file, "r")
        try:
            data = h5[dataset]
        except KeyError:
            print(f"Dataset {dataset} not found in file {file}. Skipping...")
            continue
        col_names = []
        col_val = []

        for key, value in data.items():
            if columns is None or key in columns or key == lat or key == lon:
                col_names.append(key)
                col_val.append(value[:].tolist())

        df = pd.DataFrame(map(list, zip(*col_val)), columns=col_names)
        out_df = pd.concat([out_df, df])
        h5.close()

    if nodata is not None and columns is not None:
        out_df = out_df[out_df[columns[0]] != nodata]

    gdf = gpd.GeoDataFrame(
        out_df, geometry=gpd.points_from_xy(out_df[lon], out_df[lat]), crs=crs, **kwargs
    )

    return gdf


def nasa_data_login(strategy: str = "all", persist: bool = True, **kwargs) -> None:
    """Logs in to NASA Earthdata.

    Args:
        strategy (str, optional): The authentication method.
            "all": (default) try all methods until one works
            "interactive": enter username and password.
            "netrc": retrieve username and password from ~/.netrc.
            "environment": retrieve username and password from $EARTHDATA_USERNAME and $EARTHDATA_PASSWORD.
        persist (bool, optional): Whether to persist credentials in a .netrc file. Defaults to True.
        **kwargs (Any): Additional keyword arguments for the earthaccess.login() function.
    """
    try:
        import earthaccess
    except ImportError:
        install_package("earthaccess")
        import earthaccess

    try:
        earthaccess.login(strategy=strategy, persist=persist, **kwargs)
    except:
        print(
            "Please login to Earthdata first. Register at https://urs.earthdata.nasa.gov"
        )


def nasa_data_granules_to_gdf(
    granules: List[dict], crs: str = "EPSG:4326", output: str = None, **kwargs: Any
) -> Any:
    """Converts granules data to a GeoDataFrame.

    Args:
        granules (List[dict]): A list of granules.
        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".
        output (str, optional): The output file path to save the GeoDataFrame as a file. Defaults to None.
        **kwargs: Additional keyword arguments for the gpd.GeoDataFrame.to_file() function.

    Returns:
        The resulting GeoDataFrame.
    """
    import geopandas as gpd
    import pandas as pd
    from shapely.geometry import Polygon, box

    df = pd.json_normalize([dict(i.items()) for i in granules])
    df.columns = [col.split(".")[-1] for col in df.columns]
    df = df.drop("Version", axis=1)

    def get_bbox(rectangles):
        xmin = min(rectangle["WestBoundingCoordinate"] for rectangle in rectangles)
        ymin = min(rectangle["SouthBoundingCoordinate"] for rectangle in rectangles)
        xmax = max(rectangle["EastBoundingCoordinate"] for rectangle in rectangles)
        ymax = max(rectangle["NorthBoundingCoordinate"] for rectangle in rectangles)

        bbox = (xmin, ymin, xmax, ymax)
        return bbox

    def get_polygon(coordinates):
        # Extract the points from the dictionary
        points = [
            (point["Longitude"], point["Latitude"])
            for point in coordinates[0]["Boundary"]["Points"]
        ]

        # Create a Polygon
        polygon = Polygon(points)
        return polygon

    if "BoundingRectangles" in df.columns:
        df["bbox"] = df["BoundingRectangles"].apply(get_bbox)
        df["geometry"] = df["bbox"].apply(lambda x: box(*x))
    elif "GPolygons" in df.columns:
        df["geometry"] = df["GPolygons"].apply(get_polygon)

    gdf = gpd.GeoDataFrame(df, geometry="geometry")

    gdf.crs = crs

    if output is not None:
        for column in gdf.columns:
            if gdf[column].apply(lambda x: isinstance(x, list)).any():
                gdf[column] = gdf[column].apply(lambda x: str(x))

        gdf.to_file(output, **kwargs)

    return gdf


def nasa_data_search(
    count: int = -1,
    short_name: Optional[str] = None,
    bbox: Optional[List[float]] = None,
    temporal: Optional[str] = None,
    version: Optional[str] = None,
    doi: Optional[str] = None,
    daac: Optional[str] = None,
    provider: Optional[str] = None,
    output: Optional[str] = None,
    crs: str = "EPSG:4326",
    return_gdf: bool = False,
    **kwargs: Any,
) -> Union[List[dict], tuple]:
    """Searches for NASA Earthdata granules.

    Args:
        count (int, optional): The number of granules to retrieve. Defaults to -1 (retrieve all).
        short_name (str, optional): The short name of the dataset.
        bbox (List[float], optional): The bounding box coordinates [xmin, ymin, xmax, ymax].
        temporal (str, optional): The temporal extent of the data.
        version (str, optional): The version of the dataset.
        doi (str, optional): The Digital Object Identifier (DOI) of the dataset.
        daac (str, optional): The Distributed Active Archive Center (DAAC) of the dataset.
        provider (str, optional): The provider of the dataset.
        output (str, optional): The output file path to save the GeoDataFrame as a file.
        crs (str, optional): The coordinate reference system (CRS) of the GeoDataFrame. Defaults to "EPSG:4326".
        return_gdf (bool, optional): Whether to return the GeoDataFrame in addition to the granules. Defaults to False.
        **kwargs (Any): Additional keyword arguments for the earthaccess.search_data() function.

    Returns:
        Union[List[dict], tuple]: The retrieved granules. If return_gdf is True, also returns the resulting GeoDataFrame.
    """
    try:
        import earthaccess
    except ImportError:
        install_package("earthaccess")

    if short_name is not None:
        kwargs["short_name"] = short_name
    if bbox is not None:
        kwargs["bounding_box"] = bbox
    if temporal is not None:
        kwargs["temporal"] = temporal
    if version is not None:
        kwargs["version"] = version
    if doi is not None:
        kwargs["doi"] = doi
    if daac is not None:
        kwargs["daac"] = daac
    if provider is not None:
        kwargs["provider"] = provider

    granules = earthaccess.search_data(
        count=count,
        **kwargs,
    )

    if output is not None:
        nasa_data_granules_to_gdf(granules, crs=crs, output=output)

    if return_gdf:
        gdf = nasa_data_granules_to_gdf(granules, crs=crs)
        return granules, gdf
    else:
        return granules


def nasa_data_download(
    granules: List[dict],
    out_dir: Optional[str] = None,
    provider: Optional[str] = None,
    threads: int = 8,
    keywords: Optional[List[str]] = None,
) -> None:
    """Downloads NASA Earthdata granules.

    Args:
        granules (List[dict]): The granules to download.
        out_dir (str, optional): The output directory where the granules will be downloaded. Defaults to None (current directory).
        provider (str, optional): The provider of the granules.
        threads (int, optional): The number of threads to use for downloading. Defaults to 8.
        keywords (List[str], optional): The keywords to filter the granules. Defaults to None.
    """
    import earthaccess

    if os.environ.get("USE_MKDOCS") is not None:
        return

    if keywords is None:

        earthaccess.download(
            granules, local_path=out_dir, provider=provider, threads=threads
        )
    else:

        # Collect file URLs that match any keyword
        filtered_links = []
        for granule in granules:
            for url in granule.data_links():
                if any(keyword in url for keyword in keywords):
                    filtered_links.append(url)

        if filtered_links:
            earthaccess.download(
                filtered_links, local_path=out_dir, provider=provider, threads=threads
            )
        else:
            print("No files found with the specified keywords.")


def nasa_datasets(keyword=None, df=None, return_short_name=False):
    """
    Searches for NASA datasets based on a keyword in a DataFrame.

    Args:
        keyword (str, optional): The keyword to search for. Defaults to None.
        df (pd.DataFrame, optional): The DataFrame to search in. If None, it will download the NASA dataset CSV from GitHub. Defaults to None.
        return_short_name (bool, optional): If True, only returns the list of short names of the matched datasets. Defaults to False.

    Returns:
        Union[pd.DataFrame, List[str]]: Filtered DataFrame if return_short_name is False, otherwise a list of short names.

    """
    import pandas as pd

    if df is None:
        url = "https://github.com/opengeos/NASA-Earth-Data/raw/main/nasa_earth_data.tsv"
        df = pd.read_csv(url, sep="\t")

    if keyword is not None:
        # Convert keyword and DataFrame values to lowercase
        keyword_lower = keyword.lower()
        df_lower = df.applymap(lambda x: x.lower() if isinstance(x, str) else x)

        # Use boolean indexing to filter the DataFrame
        filtered_df = df[
            df_lower.astype(str).apply(lambda x: keyword_lower in " ".join(x), axis=1)
        ].reset_index(drop=True)

        if return_short_name:
            return filtered_df["ShortName"].tolist()
        else:
            return filtered_df
    else:
        if return_short_name:
            return df["ShortName"].tolist()
        else:
            return df


def convert_coordinates(
    x, y, source_crs, target_crs="epsg:4326"
) -> Tuple[float, float]:
    """Convert coordinates from the source EPSG code to the target EPSG code.

    Args:
        x (float): The x-coordinate of the point.
        y (float): The y-coordinate of the point.
        source_crs (str): The EPSG code of the source coordinate system.
        target_crs (str, optional): The EPSG code of the target coordinate system.
            Defaults to '4326' (EPSG code for WGS84).

    Returns:
        A tuple containing the converted longitude and latitude.
    """
    import pyproj

    # Create the transformer
    transformer = pyproj.Transformer.from_crs(source_crs, target_crs, always_xy=True)

    # Perform the transformation
    lon, lat = transformer.transform(x, y)  # pylint: disable=E0633

    # Return the converted coordinates
    return lon, lat


def extract_archive(archive, outdir=None, **kwargs) -> None:
    """
    Extracts a multipart archive.

    This function uses the patoolib library to extract a multipart archive.
    If the patoolib library is not installed, it attempts to install it.
    If the archive does not end with ".zip", it appends ".zip" to the archive name.
    If the extraction fails (for example, if the files already exist), it skips the extraction.

    Args:
        archive (str): The path to the archive file.
        outdir (str): The directory where the archive should be extracted.
        **kwargs (Any): Arbitrary keyword arguments for the patoolib.extract_archive function.

    Returns:
        None

    Raises:
        Exception: An exception is raised if the extraction fails for reasons other than the files already existing.

    Example:

        files = ["sam_hq_vit_tiny.zip", "sam_hq_vit_tiny.z01", "sam_hq_vit_tiny.z02", "sam_hq_vit_tiny.z03"]
        base_url = "https://github.com/opengeos/datasets/releases/download/models/"
        urls = [base_url + f for f in files]
        leafmap.download_files(urls, out_dir="models", multi_part=True)

    """
    try:
        import patoolib
    except ImportError:
        install_package("patool")
        import patoolib

    if not archive.endswith(".zip"):
        archive = archive + ".zip"

    if outdir is None:
        outdir = os.path.dirname(archive)

    try:
        patoolib.extract_archive(archive, outdir=outdir, **kwargs)
    except Exception as e:
        print("The unzipped files might already exist. Skipping extraction.")
        return


def image_min_max(
    image: str, bands: Optional[Union[int, list]] = None
) -> Tuple[float, float]:
    """
    Computes the minimum and maximum pixel values of an image.

    This function opens an image file using xarray and rasterio, optionally
        selects specific bands, and then computes the minimum and maximum pixel
        values in the image.

    Args:
        image (str): The path to the image file.
        bands (int or list, optional): The band or list of bands to select. If
            None, all bands are used.

    Returns:
        Tuple[float, float]: The minimum and maximum pixel values in the image.
    """

    import rioxarray
    import xarray as xr

    dataset = xr.open_dataset(image, engine="rasterio")

    if bands is not None:
        dataset = dataset.sel(band=bands)

    vmin = dataset["band_data"].min().values.item()
    vmax = dataset["band_data"].max().values.item()

    return vmin, vmax


def pandas_to_geojson(
    df,
    coordinates=["lng", "lat"],
    geometry_type: str = "Point",
    properties: list = None,
    output: Optional[str] = None,
) -> dict:
    """
    Convert a DataFrame to a GeoJSON format.

    Args:
        df (pd.DataFrame): The input DataFrame containing the data.
        coordinates (list): A list of two column names representing the
            longitude and latitude coordinates.
        geometry_type (str): The type of geometry for the GeoJSON features
            (e.g., "Point", "LineString", "Polygon").
        properties (list): A list of column names to include in the properties
            of each GeoJSON feature. If None, all columns except the coordinate
            columns are included.
        output (str, optional): The file path to save the GeoJSON output. If None,
            the GeoJSON is not saved to a file.

    Returns:
        A dictionary representing the GeoJSON object.
    """

    import pandas as pd

    if isinstance(df, str):
        if df.endswith(".csv"):
            df = pd.read_csv(df)
        elif df.endswith(".json"):
            df = pd.read_json(df)
        else:
            raise ValueError("The input file must be a CSV or JSON file.")

    geojson = {"type": "FeatureCollection", "features": []}

    if properties is None:
        properties = [col for col in df.columns if col not in coordinates]

    for _, row in df.iterrows():
        feature = {
            "type": "Feature",
            "properties": {},
            "geometry": {"type": geometry_type, "coordinates": []},
        }
        feature["geometry"]["coordinates"] = list(row[coordinates])
        for prop in properties:
            feature["properties"][prop] = row[prop]

        geojson["features"].append(feature)

    if output:
        with open(output, "w") as f:
            json.dump(geojson, f, indent=4)

    return geojson


def replace_top_level_hyphens(d: Union[Dict, Any]) -> Union[Dict, Any]:
    """
    Replaces hyphens with underscores in top-level dictionary keys.

    Args:
        d (Union[Dict, Any]): The input dictionary or any other data type.

    Returns:
        Union[Dict, Any]: The modified dictionary with top-level keys having hyphens replaced with underscores,
        or the original input if it's not a dictionary.
    """
    if isinstance(d, dict):
        return {k.replace("-", "_"): v for k, v in d.items()}
    return d


def replace_hyphens_in_keys(d: Union[Dict, List, Any]) -> Union[Dict, List, Any]:
    """
    Recursively replaces hyphens with underscores in dictionary keys.

    Args:
        d (Union[Dict, List, Any]): The input dictionary, list or any other data type.

    Returns:
        Union[Dict, List, Any]: The modified dictionary or list with keys having hyphens replaced with underscores,
        or the original input if it's not a dictionary or list.
    """
    if isinstance(d, dict):
        return {k.replace("-", "_"): replace_hyphens_in_keys(v) for k, v in d.items()}
    elif isinstance(d, list):
        return [replace_hyphens_in_keys(i) for i in d]
    else:
        return d


def replace_underscores_in_keys(d: Union[Dict, List, Any]) -> Union[Dict, List, Any]:
    """
    Recursively replaces underscores with hyphens in dictionary keys.

    Args:
        d (Union[Dict, List, Any]): The input dictionary, list or any other data type.

    Returns:
        Union[Dict, List, Any]: The modified dictionary or list with keys having underscores replaced with hyphens,
        or the original input if it's not a dictionary or list.
    """
    if isinstance(d, dict):
        return {
            k.replace("_", "-"): replace_underscores_in_keys(v) for k, v in d.items()
        }
    elif isinstance(d, list):
        return [replace_underscores_in_keys(i) for i in d]
    else:
        return d


def geojson_bounds(geojson: dict) -> Optional[list]:
    """
    Calculate the bounds of a GeoJSON object.

    This function uses the shapely library to calculate the bounds of a GeoJSON object.
    If the shapely library is not installed, it will print a message and return None.

    Args:
        geojson (dict): A dictionary representing a GeoJSON object.

    Returns:
        A list of bounds (minx, miny, maxx, maxy) if shapely is installed, None otherwise.
    """
    try:
        import shapely
    except ImportError:
        print("shapely is not installed")
        return

    if isinstance(geojson, str):
        geojson = json.loads(geojson)

    return list(shapely.bounds(shapely.from_geojson(json.dumps(geojson))))


def execute_notebook(in_file):
    """Executes a Jupyter notebook and save output cells

    Args:
        in_file (str): Input Jupyter notebook.
    """
    # command = 'jupyter nbconvert --to notebook --execute ' + in_file + ' --inplace'
    command = 'jupyter nbconvert --to notebook --execute "{}" --inplace'.format(in_file)
    print(os.popen(command).read().rstrip())
    # os.popen(command)


def execute_notebook_dir(in_dir):
    """Executes all Jupyter notebooks in the given directory recursively and save output cells.

    Args:
        in_dir (str): Input folder containing notebooks.
    """
    from pathlib import Path

    in_dir = os.path.abspath(in_dir)

    files = list(Path(in_dir).rglob("*.ipynb"))
    files.sort()
    count = len(files)
    if files is not None:
        for index, file in enumerate(files):
            in_file = str(file)
            print(f"Processing {index + 1}/{count}: {file} ...")
            execute_notebook(in_file)


def execute_maplibre_notebook_dir(
    in_dir: str,
    out_dir: str,
    delete_html: bool = True,
    replace_api_key: bool = True,
    recursive: bool = False,
    keep_notebook: bool = False,
    index_html: bool = True,
    ignore_files: Optional[List[str]] = None,
) -> None:
    """
    Executes Jupyter notebooks found in a specified directory, optionally replacing API keys and deleting HTML outputs.

    Args:
        in_dir (str): The input directory containing Jupyter notebooks to be executed.
        out_dir (str): The output directory where the executed notebooks and their HTML outputs will be saved.
        delete_html (bool, optional): If True, deletes any existing HTML files in the output directory before execution. Defaults to True.
        replace_api_key (bool, optional): If True, replaces the API key in the output HTML. Defaults to True.
            set "MAPTILER_KEY" and "MAPTILER_KEY_PUBLIC" to your MapTiler API key and public key, respectively.
        recursive (bool, optional): If True, searches for notebooks in the input directory recursively. Defaults to False.
        keep_notebook (bool, optional): If True, keeps the executed notebooks in the output directory. Defaults to False.
        index_html (bool, optional): If True, generates an index.html file in the output directory listing all files. Defaults to True.
        ignore_files (list, optional): A list of notebook files to ignore during execution. Defaults to None.

    Returns:
        None
    """
    import shutil

    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    if replace_api_key:
        os.environ["MAPTILER_REPLACE_KEY"] = "True"

    if delete_html:
        html_files = find_files(out_dir, "*.html", recursive=recursive)
        for file in html_files:
            os.remove(file)

    if ignore_files is not None:
        ignore_files = [os.path.join(in_dir, f) for f in ignore_files]

    files = find_files(in_dir, "*.ipynb", recursive=recursive)
    for index, file in enumerate(files):
        print(f"Processing {index + 1}/{len(files)}: {file} ...")

        if ignore_files is not None and file in ignore_files:
            print(f"Skipping {file} ...")
            continue

        basename = os.path.basename(file)
        out_file = os.path.join(out_dir, basename)
        shutil.copy(file, out_file)

        with open(out_file, "r") as f:
            lines = f.readlines()

        out_lines = []
        for line in lines:
            if line.strip() == '"m"':
                title = os.path.splitext(basename)[0].replace("_", " ")
                out_lines.append(line.replace("m", f"m.to_html(title='{title}')"))
            else:
                out_lines.append(line)

        with open(out_file, "w") as f:
            f.writelines(out_lines)

        out_html = os.path.basename(out_file).replace(".ipynb", ".html")
        os.environ["MAPLIBRE_OUTPUT"] = out_html
        execute_notebook(out_file)

    if not keep_notebook:
        all_files = find_files(out_dir, "*", recursive=recursive)
        for file in all_files:
            if not file.endswith(".html"):
                os.remove(file)

    if index_html:
        generate_index_html(out_dir)


def generate_index_html(directory: str, output: str = "index.html") -> None:
    """
    Generates an HTML file named 'index.html' in the specified directory, listing
    all files in that directory as clickable links.

    Args:
        directory (str): The path to the directory for which to generate the index.html file.
        output (str, optional): The name of the output HTML file. Defaults to "index.html".

    Returns:
        None
    """
    # Get a list of files in the directory
    files = sorted(
        [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))]
    )

    # Start the HTML content
    html_content = """<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Index of {directory}</title>
</head>
<body>
    <h1>Index of {directory}</h1>
    <ul>
""".format(
        directory=directory
    )

    # Add each file to the HTML list
    for file in files:
        html_content += '        <li><a href="{file}">{file}</a></li>\n'.format(
            file=file
        )

    # Close the HTML content
    html_content += """    </ul>
</body>
</html>"""

    # Write the HTML content to index.html in the specified directory
    with open(os.path.join(directory, output), "w") as f:
        f.write(html_content)


def github_get_release_id_by_tag(username, repository, tag_name, access_token=None):
    """
    Fetches the release ID by tag name for a given GitHub repository.

    Args:
        username (str): GitHub username or organization name.
        repository (str): Name of the GitHub repository.
        tag_name (str): Tag name of the release.
        access_token (str, optional): Personal access token for authentication. Defaults to None.

    Returns:
        The release ID if found, None otherwise.
    """

    if access_token is None:
        access_token = get_api_key("GITHUB_API_TOKEN")

    # GitHub API URL for fetching releases
    url = (
        f"https://api.github.com/repos/{username}/{repository}/releases/tags/{tag_name}"
    )

    # Headers for authentication (optional)
    headers = {"Authorization": f"token {access_token}"} if access_token else {}

    # Make the request to the GitHub API
    response = requests.get(url, headers=headers)

    # Check if the request was successful
    if response.status_code == 200:
        release_info = response.json()
        return release_info.get("id")
    else:
        print(
            f"Error: Unable to fetch release info for tag {tag_name} (Status code: {response.status_code})"
        )
        return None


def github_get_release_assets(username, repository, release_id, access_token=None):
    """
    Fetches the assets for a given release.

    Args:
        username (str): GitHub username or organization name.
        repository (str): Name of the GitHub repository.
        release_id (int): ID of the release to fetch assets for.
        access_token (str): Personal access token for authentication.

    Returns:
        List of assets if successful, None otherwise.
    """
    if access_token is None:
        access_token = get_api_key("GITHUB_API_TOKEN")
    url = f"https://api.github.com/repos/{username}/{repository}/releases/{release_id}/assets"
    headers = {
        "Authorization": f"token {access_token}",
        "Accept": "application/vnd.github.v3+json",
    }

    response = requests.get(url, headers=headers)

    if response.status_code == 200:
        return response.json()
    else:
        print(f"Error: Unable to fetch assets (Status code: {response.status_code})")
        return None


def github_delete_asset(username, repository, asset_id, access_token=None):
    """
    Deletes an asset from a GitHub release.

    Args:
        username (str): GitHub username or organization name.
        repository (str): Name of the GitHub repository.
        asset_id (int): ID of the asset to delete.
        access_token (str): Personal access token for authentication.
    """
    if access_token is None:
        access_token = get_api_key("GITHUB_API_TOKEN")
    url = f"https://api.github.com/repos/{username}/{repository}/releases/assets/{asset_id}"
    headers = {
        "Authorization": f"token {access_token}",
        "Accept": "application/vnd.github.v3+json",
    }

    response = requests.delete(url, headers=headers)

    if response.status_code == 204:
        print(f"Successfully deleted asset ID: {asset_id}")
    else:
        print(f"Error: Unable to delete asset (Status code: {response.status_code})")


def github_upload_asset_to_release(
    username, repository, release_id, asset_path, quiet=False, access_token=None
):
    """
    Uploads an asset to a GitHub release.

    Args:
        username (str): GitHub username or organization name.
        repository (str): Name of the GitHub repository.
        release_id (int): ID of the release to upload the asset to.
        asset_path (str): Path to the asset file.

        access_token (str): Personal access token for authentication.

    Returns:
        The response JSON from the GitHub API if the upload is successful.
        None: If the upload fails.
    """
    if access_token is None:
        access_token = get_api_key("GITHUB_API_TOKEN")
    # GitHub API URL for uploading release assets
    url = f"https://uploads.github.com/repos/{username}/{repository}/releases/{release_id}/assets"

    # Extract the filename from the asset path
    asset_name = os.path.basename(asset_path)

    # Set the headers for the upload request
    headers = {
        "Authorization": f"token {access_token}",
        "Content-Type": "application/octet-stream",
    }

    # Set the parameters for the upload request
    params = {"name": asset_name}

    # Check if the asset already exists
    assets = github_get_release_assets(username, repository, release_id, access_token)
    if assets:
        for asset in assets:
            if asset["name"] == asset_name:
                github_delete_asset(username, repository, asset["id"], access_token)
                break

    # Open the asset file in binary mode
    with open(asset_path, "rb") as asset_file:
        # Make the request to upload the asset
        response = requests.post(url, headers=headers, params=params, data=asset_file)

    # Check if the request was successful
    if response.status_code == 201:
        print(f"Successfully uploaded asset: {asset_name}")
        if not quiet:
            return response.json()
        else:
            return None
    else:
        print(f"Error: Unable to upload asset (Status code: {response.status_code})")
        if not quiet:
            print(response.json())
        return None


def remove_port_from_string(data: str) -> str:
    """
    Removes the port number from all URLs in the given string.

    Args::
        data (str): The input string containing URLs.

    Returns:
        The string with port numbers removed from all URLs.
    """
    import re

    # Regular expression to match URLs with port numbers
    url_with_port_pattern = re.compile(r"(http://[\d\w.]+):\d+")

    # Function to remove the port from the matched URLs
    def remove_port(match):
        return match.group(1)

    # Substitute the URLs with ports removed
    result = url_with_port_pattern.sub(remove_port, data)

    return result


def xarray_to_raster(dataset, filename: str, **kwargs: Dict[str, Any]) -> None:
    """Convert an xarray Dataset to a raster file.

    Args:
        dataset (xr.Dataset): The input xarray Dataset to be converted.
        filename (str): The output filename for the raster file.
        **kwargs (Dict[str, Any]): Additional keyword arguments passed to the `rio.to_raster()` method.
            See https://corteva.github.io/rioxarray/stable/examples/convert_to_raster.html for more info.

    Returns:
        None
    """
    import rioxarray

    dims = list(dataset.dims)

    new_names = {}

    if "lat" in dims:
        new_names["lat"] = "y"
        dims.remove("lat")
    if "lon" in dims:
        new_names["lon"] = "x"
        dims.remove("lon")
    if "lng" in dims:
        new_names["lng"] = "x"
        dims.remove("lng")
    if "latitude" in dims:
        new_names["latitude"] = "y"
        dims.remove("latitude")
    if "longitude" in dims:
        new_names["longitude"] = "x"
        dims.remove("longitude")

    dataset = dataset.rename(new_names)
    dataset.transpose(..., "y", "x").rio.to_raster(filename, **kwargs)


def ee_tile_url(
    ee_object=None,
    vis_params={},
    asset_id: str = None,
    ee_initialize: bool = False,
    project_id=None,
    **kwargs: Any,
) -> None:
    """
    Adds a Google Earth Engine tile layer to the map based on the tile layer URL from
        https://github.com/opengeos/ee-tile-layers/blob/main/datasets.tsv.

    Args:
        ee_object (object): The Earth Engine object to display.
        vis_params (dict): Visualization parameters. For example, {'min': 0, 'max': 100}.
        asset_id (str): The ID of the Earth Engine asset.
        ee_initialize (bool, optional): Whether to initialize the Earth Engine

    Returns:
        None
    """
    import pandas as pd

    if isinstance(asset_id, str):
        df = pd.read_csv(
            "https://raw.githubusercontent.com/opengeos/ee-tile-layers/main/datasets.tsv",
            sep="\t",
        )

        asset_id = asset_id.strip()

        if asset_id in df["id"].values:
            url = df.loc[df["id"] == asset_id, "url"].values[0]
            return url
        else:
            print(f"The provided EE tile layer {asset_id} does not exist.")
            return None
    elif ee_object is not None:
        try:
            import geemap
            from geemap.ee_tile_layers import _get_tile_url_format

            if ee_initialize:
                geemap.ee_initialize(project=project_id, **kwargs)
            url = _get_tile_url_format(ee_object, vis_params)
            return url
        except Exception as e:
            print(e)
            return None


def d2s_tile(url: str, titiler_endpoint: str = None, **kwargs: Any) -> str:
    """Generate a D2S tile URL with optional API key.

    Args:
        url (str): The base URL for the tile.
        titiler_endpoint (str, optional): The endpoint for the titiler service.
            Defaults to "https://tt.d2s.org".
        **kwargs (Any): Additional keyword arguments to pass to the cog_stats function.

    Returns:
        The modified URL with the API key if required, otherwise the original URL.

    Raises:
        ValueError: If the API key is required but not set in the environment variables.
    """

    if titiler_endpoint is None:
        titiler_endpoint = os.environ.get(
            "TITILER_ENDPOINT", "https://giswqs-titiler-endpoint.hf.space"
        )

    stats = cog_stats(url, titiler_endpoint=titiler_endpoint, **kwargs)
    if "detail" in stats:
        api_key = get_api_key("D2S_API_KEY")
        if api_key is None:
            raise ValueError("Please set the D2S_API_KEY environment variable.")
        else:
            return f"{url}?API_KEY={api_key}"
    else:
        return url


def convert_to_gdf(
    data: Union[pd.DataFrame, str],
    geometry: Optional[str] = None,
    lat: Optional[str] = None,
    lon: Optional[str] = None,
    crs: str = "EPSG:4326",
    included: Optional[List[str]] = None,
    excluded: Optional[List[str]] = None,
    obj_to_str: bool = False,
    open_args: Optional[Dict[str, Any]] = None,
    **kwargs: Any,
) -> "gpd.GeoDataFrame":
    """Convert data to a GeoDataFrame.

    Args:
        data (Union[pd.DataFrame, str]): The input data, either as a DataFrame or a file path.
        geometry (Optional[str], optional): The column name containing geometry data. Defaults to None.
        lat (Optional[str], optional): The column name containing latitude data. Defaults to None.
        lon (Optional[str], optional): The column name containing longitude data. Defaults to None.
        crs (str, optional): The coordinate reference system to use. Defaults to "EPSG:4326".
        included (Optional[List[str]], optional): List of columns to include. Defaults to None.
        excluded (Optional[List[str]], optional): List of columns to exclude. Defaults to None.
        obj_to_str (bool, optional): Whether to convert object dtype columns to string. Defaults to False.
        open_args (Optional[Dict[str, Any]], optional): Additional arguments for file opening functions. Defaults to None.
        **kwargs (Any): Additional keyword arguments for GeoDataFrame creation.

    Returns:
        The converted GeoDataFrame.

    Raises:
        ValueError: If the file format is unsupported or required columns are not provided.
    """
    import geopandas as gpd
    from shapely.geometry import Point, shape

    if open_args is None:
        open_args = {}

    if not isinstance(data, pd.DataFrame):
        if isinstance(data, str):
            if data.endswith(".parquet"):
                data = pd.read_parquet(data, **open_args)
            elif data.endswith(".csv"):
                data = pd.read_csv(data, **open_args)
            elif data.endswith(".json"):
                data = pd.read_json(data, **open_args)
            elif data.endswith(".xlsx"):
                data = pd.read_excel(data, **open_args)
            else:
                raise ValueError(
                    "Unsupported file format. Only Parquet, CSV, JSON, and Excel files are supported."
                )

    # If include_cols is specified, filter the DataFrame to include only those columns
    if included:
        if geometry:
            included.append(geometry)
        elif lat and lon:
            included.append(lat)
            included.append(lon)
        data = data[included]

    # Exclude specified columns if provided
    if excluded:
        data = data.drop(columns=excluded)

    # Convert 'object' dtype columns to 'string' if obj_to_str is True
    if obj_to_str:
        data = data.astype(
            {col: "string" for col in data.select_dtypes(include="object").columns}
        )

    # Handle the creation of geometry
    if geometry:

        def convert_geometry(x):
            if isinstance(x, str):
                try:
                    # Parse the string as JSON and then convert to a geometry
                    return shape(json.loads(x))
                except (json.JSONDecodeError, TypeError) as e:
                    print(f"Error converting geometry: {e}")
                    return None
            return x

        data = data[data[geometry].notnull()]
        data[geometry] = data[geometry].apply(convert_geometry)
    elif lat and lon:
        # Create a geometry column from latitude and longitude
        data["geometry"] = data.apply(lambda row: Point(row[lon], row[lat]), axis=1)
        geometry = "geometry"
    else:
        raise ValueError(
            "Either geometry_col or both lat_col and lon_col must be provided."
        )

    # Convert the DataFrame to a GeoDataFrame
    gdf = gpd.GeoDataFrame(data, geometry=geometry, **kwargs)

    # Set CRS (assuming WGS84 by default, modify as needed)
    gdf.set_crs(crs, inplace=True)

    return gdf


def points_to_line(
    data: Union[str, pd.DataFrame],
    src_lat: str,
    src_lon: str,
    dst_lat: str,
    dst_lon: str,
    crs: str = "EPSG:4326",
    **kwargs: Any,
) -> "gpd.GeoDataFrame":
    """
    Converts source and destination coordinates into a GeoDataFrame with LineString geometries.

    Args:
        data (Union[str, pd.DataFrame, gpd.GeoDataFrame]): Input data which can be a file path or a DataFrame.
        src_lat (str): Column name for source latitude.
        src_lon (str): Column name for source longitude.
        dst_lat (str): Column name for destination latitude.
        dst_lon (str): Column name for destination longitude.
        crs (str, optional): Coordinate reference system. Defaults to "EPSG:4326".
        **kwargs (Any): Additional arguments passed to the file reading functions.

    Returns:
        A GeoDataFrame with LineString geometries.
    """
    import geopandas as gpd
    from shapely.geometry import LineString

    if isinstance(data, str):
        if data.endswith(".parquet"):
            gdf = pd.read_parquet(data, **kwargs)
        elif data.endswith(".csv"):
            gdf = pd.read_csv(data, **kwargs)
        elif data.endswith(".json"):
            gdf = pd.read_json(data, **kwargs)
        elif data.endswith(".xlsx"):
            gdf = pd.read_excel(data, **kwargs)
        else:
            gdf = gpd.read_file(data, **kwargs)

    elif isinstance(data, pd.DataFrame) or isinstance(data, gpd.GeoDataFrame):
        gdf = data.copy()
    else:
        raise ValueError(
            "Unsupported data type. Please provide a file path or a DataFrame."
        )

    # Assuming you have a GeoDataFrame 'gdf' with the source and destination coordinates
    def create_polyline(row):
        source_point = (row[src_lon], row[src_lat])
        dst_point = (row[dst_lon], row[dst_lat])
        return LineString([source_point, dst_point])

    # Apply the function to create the polyline geometry
    gdf["geometry"] = gdf.apply(create_polyline, axis=1)

    # Set the GeoDataFrame's geometry column to the newly created geometry column
    gdf = gdf.set_geometry("geometry")
    gdf.crs = crs
    return gdf


def read_file(data: str, **kwargs: Any) -> Union[pd.DataFrame, "gpd.GeoDataFrame"]:
    """
    Reads a file and returns a DataFrame or GeoDataFrame.

    Args:
        data (str): The file path or a DataFrame/GeoDataFrame.
        **kwargs (Any): Additional arguments passed to the file reading function.

    Returns:
        Union[pd.DataFrame, gpd.GeoDataFrame]: The read data as a DataFrame or GeoDataFrame.

    Raises:
        ValueError: If the data type is unsupported.
    """
    import geopandas as gpd

    if isinstance(data, str):
        if data.endswith(".parquet"):
            df = pd.read_parquet(data, **kwargs)
        elif data.endswith(".csv"):
            df = pd.read_csv(data, **kwargs)
        elif data.endswith(".json"):
            df = pd.read_json(data, **kwargs)
        elif data.endswith(".xlsx"):
            df = pd.read_excel(data, **kwargs)
        else:
            df = gpd.read_file(data, **kwargs)
    elif isinstance(data, dict) or isinstance(data, list):
        df = pd.DataFrame(data, **kwargs)

    elif isinstance(data, pd.DataFrame) or isinstance(data, gpd.GeoDataFrame):
        df = data
    else:
        raise ValueError(
            "Unsupported data type. Please provide a file path or a DataFrame."
        )

    return df


def get_overture_data(
    overture_type: str,
    bbox: Tuple[float, float, float, float] = None,
    columns: List[str] = None,
    output: str = None,
) -> "gpd.GeoDataFrame":
    """Fetches overture data and returns it as a GeoDataFrame.

    Args:
        overture_type (str): The type of overture data to fetch.It can be one of the following:
            address|building|building_part|division|division_area|division_boundary|place|
            segment|connector|infrastructure|land|land_cover|land_use|water
        bbox (Tuple[float, float, float, float], optional): The bounding box to
            filter the data. Defaults to None.
        columns (List[str], optional): The columns to include in the output.
            Defaults to None.
        output (str, optional): The file path to save the output GeoDataFrame.
            Defaults to None.

    Returns:
        The fetched overture data as a GeoDataFrame.

    Raises:
        ImportError: If the overture package is not installed.
    """

    try:
        from overturemaps import core
    except ImportError:
        install_package("overturemaps")
        from overturemaps import core

    gdf = core.geodataframe(overture_type, bbox=bbox)
    if columns is not None:
        gdf = gdf[columns]

    gdf.crs = "EPSG:4326"
    if output is not None:
        gdf.to_file(output)

    return gdf


def construct_bbox(
    *args: Union[float, Tuple[float, float, float, float]],
    buffer: float = 0.001,
    crs: str = "EPSG:4326",
    return_gdf: bool = False,
) -> Union["Polygon", "gpd.GeoDataFrame"]:
    """
    Construct a bounding box (bbox) geometry based on either a centroid point or bbox.

    Args:
        *args: Coordinates for the geometry.
            - If 2 arguments are provided, it is interpreted as a centroid (x, y) with a buffer.
            - If 4 arguments are provided, it is interpreted as a bbox (minx, miny, maxx, maxy).
        buffer (float): The buffer distance around the centroid point (default is 0.01 degrees).
        crs (str): The coordinate reference system (default is "EPSG:4326").
        return_gdf (bool): Whether to return a GeoDataFrame (default is False).

    Returns:
        The constructed bounding box (Polygon).
    """
    from shapely.geometry import box

    if len(args) == 2:
        # Case 1: Create a bounding box around the centroid point with a buffer
        x, y = args
        minx, miny = x - buffer, y - buffer
        maxx, maxy = x + buffer, y + buffer
        geometry = box(minx, miny, maxx, maxy)

    elif len(args) == 4:
        # Case 2: Create a bounding box directly from the given coordinates
        geometry = box(args[0], args[1], args[2], args[3])

    else:
        raise ValueError(
            "Provide either 2 arguments for centroid (x, y) or 4 arguments for bbox (minx, miny, maxx, maxy)."
        )

    if return_gdf:
        return gpd.GeoDataFrame(geometry=[geometry], columns=["geometry"], crs=crs)
    else:
        return geometry


def get_nhd(
    geometry: Union[
        "gpd.GeoDataFrame", str, List[float], Tuple[float, float, float, float]
    ],
    geo_crs: int = 4326,
    xy: bool = True,
    buffer: float = 0.001,
    dataset: str = "wbd08",
    predicate: str = "intersects",
    sort_attr: Optional[str] = None,
    **kwargs: Any,
) -> Optional["gpd.GeoDataFrame"]:
    """
    Fetches National Hydrography Dataset (NHD) data based on the provided geometry.

    Args:
        geometry (Union[gpd.GeoDataFrame, str, List[float], Tuple[float, float, float, float]]):
            The geometry to query the NHD data. It can be a GeoDataFrame, a file path, or coordinates.
        geo_crs (int): The coordinate reference system (CRS) of the geometry (default is 4326).
        xy (bool): Whether to use x, y coordinates (default is True).
        buffer (float): The buffer distance around the centroid point (default is 0.001 degrees).
        dataset (str): The NHD dataset to query (default is "wbd08").
        predicate (str): The spatial predicate to use for the query (default is "intersects").
        sort_attr (Optional[str]): The attribute to sort the results by (default is None).
        **kwargs: Additional keyword arguments to pass to the WaterData.bygeom method.

    Returns:
        The fetched NHD data as a GeoDataFrame, or None if an error occurs.

    Raises:
        ImportError: If the pynhd package is not installed.
        ValueError: If the geometry type is unsupported.
    """
    try:
        import pynhd
    except ImportError:
        print("The pynhd package is required for this function. Installing...")
        install_package("pynhd")

    import geopandas as gpd
    from pynhd import WaterData

    if isinstance(geometry, (list, tuple)):
        crs = f"EPSG:{geo_crs}"
        geometry = construct_bbox(*geometry, buffer=buffer, crs=crs, return_gdf=False)
    elif isinstance(geometry, gpd.GeoDataFrame):
        geometry = geometry.union_all()
    elif isinstance(geometry, str):
        geometry = gpd.read_file(geometry).union_all()

    water_data = WaterData(dataset)

    try:
        gdf = water_data.bygeom(geometry, geo_crs, xy, predicate, sort_attr, **kwargs)
    except Exception as e:
        print(e)
        gdf = None

    return gdf


def _convert_geometry_to_esri_format(geometry):
    """
    Convert shapely geometry to ESRI's format for point, polygon, polyline, or multipoint.

    Args:
        geometry (shapely.geometry.base.BaseGeometry): The shapely geometry to convert.

    Returns:
        The geometry in ESRI format.
    """
    from shapely.geometry import LineString, MultiPoint, Point, Polygon

    if isinstance(geometry, Point):
        # Convert point to ESRI format
        return {"x": geometry.x, "y": geometry.y}
    elif isinstance(geometry, Polygon):
        # Convert polygon to ESRI format (rings)
        return {"rings": [list(geometry.exterior.coords)]}
    elif isinstance(geometry, LineString):
        # Convert polyline to ESRI format (paths)
        return {"paths": [list(geometry.coords)]}
    elif isinstance(geometry, MultiPoint):
        # Convert multipoint to ESRI format (points)
        return {"points": [list(point.coords[0]) for point in geometry.geoms]}
    else:
        raise ValueError(f"Unsupported geometry type: {type(geometry)}")


def _convert_geodataframe_to_esri_format(gdf: "gpd.GeoDataFrame"):
    """
    Convert all geometries in a GeoDataFrame to ESRI's format.

    Args:
        gdf (geopandas.GeoDataFrame): A GeoDataFrame containing geometries.

    Returns:
        A list of geometries in ESRI format.
    """
    esri_geometries = []

    for geom in gdf.geometry:
        esri_format = _convert_geometry_to_esri_format(geom)
        esri_geometries.append(esri_format)

    return esri_geometries


def get_nwi(
    geometry: Dict[str, Any],
    in_sr: str = "4326",
    out_sr: str = "3857",
    spatial_rel: str = "esriSpatialRelIntersects",
    return_geometry: bool = True,
    out_fields: str = None,
    clip: bool = False,
    add_class: bool = False,
    output: Optional[str] = None,
    **kwargs: Any,
) -> Union["gpd.GeoDataFrame", "pd.DataFrame", Dict[str, str]]:
    """
    Query the NWI (National Wetlands Inventory) API using various geometry types.
    https://fwspublicservices.wim.usgs.gov/wetlandsmapservice/rest/services/Wetlands/FeatureServer

    Args:
        geometry (dict): The geometry data (e.g., point, polygon, polyline, multipoint, etc.).
        in_sr (str): The input spatial reference (default is EPSG:4326).
        out_sr (str): The output spatial reference (default is EPSG:3857).
        spatial_rel (str): The spatial relationship (default is "esriSpatialRelIntersects").
        return_geometry (bool): Whether to return the geometry (default is True).
        out_fields (str): The fields to be returned (default is None). Can be "*" or a
            comma-separated list of fields. The field names start with "Wetlands." or "NWI_Wetland_Codes."
        clip (bool): Whether to clip the geometry to the input geometry (default is False).
        add_class (bool): Whether to add a unique integer class column to the output GeoDataFrame (default is False).
        output (str): The output file path to save the GeoDataFrame (default is None).
        **kwargs: Additional keyword arguments to pass to the API.

    Returns:
        The queried NWI data as a GeoDataFrame.
    """

    import geopandas as gpd
    import pandas as pd
    from shapely.geometry import Polygon

    if out_fields is None:
        out_fields = "Wetlands.OBJECTID, Wetlands.ATTRIBUTE, Wetlands.WETLAND_TYPE, Wetlands.ACRES, Wetlands.Shape_Length, Wetlands.Shape_Area"

    def detect_geometry_type(geometry):
        """
        Automatically detect the geometry type based on the structure of the geometry dictionary.
        """
        if "x" in geometry and "y" in geometry:
            return "esriGeometryPoint"
        elif (
            "xmin" in geometry
            and "ymin" in geometry
            and "xmax" in geometry
            and "ymax" in geometry
        ):
            return "esriGeometryEnvelope"
        elif "rings" in geometry:
            return "esriGeometryPolygon"
        elif "paths" in geometry:
            return "esriGeometryPolyline"
        elif "points" in geometry:
            return "esriGeometryMultipoint"
        else:
            raise ValueError("Unsupported geometry type or invalid geometry structure.")

    # Convert GeoDataFrame to a dictionary if needed
    geometry_type = None
    if isinstance(geometry, gpd.GeoDataFrame):
        geometry_dict = _convert_geodataframe_to_esri_format(geometry)[0]
        geometry_type = detect_geometry_type(geometry_dict)
    elif isinstance(geometry, dict):
        geometry_type = detect_geometry_type(geometry)
        geometry_dict = geometry
    elif isinstance(geometry, str):
        geometry_dict = geometry
    elif isinstance(geometry, list) and len(geometry) == 4:
        geometry_dict = {
            "xmin": geometry[0],
            "ymin": geometry[1],
            "xmax": geometry[2],
            "ymax": geometry[3],
        }
        geometry_type = "esriGeometryEnvelope"
    else:
        raise ValueError(
            "Invalid geometry input. Must be a GeoDataFrame or a dictionary."
        )

    # Convert geometry to a JSON string (required by the API)
    if isinstance(geometry_dict, dict):
        geometry_json = json.dumps(geometry_dict)
    else:
        geometry_json = geometry_dict
    # API URL for querying wetlands
    url = "https://fwspublicservices.wim.usgs.gov/wetlandsmapservice/rest/services/Wetlands/MapServer/0/query"

    # Construct the query parameters
    params = {
        "geometry": geometry_json,  # The geometry as a JSON string
        "geometryType": geometry_type,  # Geometry type (automatically detected)
        "inSR": in_sr,  # Spatial reference system (default is WGS84)
        "spatialRel": spatial_rel,  # Spatial relationship (default is intersects)
        "outFields": out_fields,  # Which fields to return (default is all fields)
        "returnGeometry": str(
            return_geometry
        ).lower(),  # Whether to return the geometry
        "f": "json",  # Response format
    }

    for key, value in kwargs.items():
        params[key] = value

    # Make the GET request
    response = requests.get(url, params=params)

    # Check if the request was successful
    if response.status_code == 200:
        data = response.json()  # Return the data as a Python dictionary
    else:
        return {"error": f"Request failed with status code {response.status_code}"}

    # Extract the features
    features = data["features"]

    # Prepare the attribute data and geometries
    attributes = [feature["attributes"] for feature in features]

    # Create a DataFrame for attributes
    df = pd.DataFrame(attributes)
    if "NWI_Wetland_Codes.OBJECTID" in df.columns:
        df.drop(
            columns=["NWI_Wetland_Codes.OBJECTID", "NWI_Wetland_Codes.ATTRIBUTE"],
            inplace=True,
        )

    df.columns = [column.split(".")[-1] for column in df.columns]
    df.rename(columns={"WETLAND_TYPE": "WETLAND_TY"}, inplace=True)

    if return_geometry:
        geometries = [Polygon(feature["geometry"]["rings"][0]) for feature in features]
        # Create a GeoDataFrame by combining the attributes and geometries
        gdf = gpd.GeoDataFrame(
            df,
            geometry=geometries,
            crs=f"EPSG:{data['spatialReference']['latestWkid']}",
        )
        if out_sr != "3857":
            gdf = gdf.to_crs(out_sr)

        if clip:
            gdf = clip_vector(gdf, clip_geom=geometry)

        if add_class:
            gdf = add_unique_class(gdf, "WETLAND_TY")

        if output is not None:
            gdf.to_file(output)

        return gdf
    else:
        return df


def get_wbd(
    geometry: Union["gpd.GeoDataFrame", Dict[str, Any]] = None,
    searchText: Optional[str] = None,
    inSR: str = "4326",
    outSR: str = "3857",
    digit: int = 8,
    spatialRel: str = "esriSpatialRelIntersects",
    return_geometry: bool = True,
    outFields: str = "*",
    output: Optional[str] = None,
    **kwargs: Any,
) -> Union["gpd.GeoDataFrame", "pd.DataFrame", Dict[str, str]]:
    """
    Query the WBD (Watershed Boundary Dataset) API using various geometry types or a GeoDataFrame.
    https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServer

    Args:
        geometry (Union[gpd.GeoDataFrame, Dict]): The geometry data (GeoDataFrame or geometry dict).
        inSR (str): The input spatial reference (default is EPSG:4326).
        outSR (str): The output spatial reference (default is EPSG:3857).
        digit (int): The digit code for the WBD layer (default is 8).
        spatialRel (str): The spatial relationship (default is "esriSpatialRelIntersects").
        return_geometry (bool): Whether to return the geometry (default is True).
        outFields (str): The fields to be returned (default is "*").
        output (Optional[str]): The output file path to save the GeoDataFrame (default is None).
        **kwargs: Additional keyword arguments to pass to the API.

    Returns:
        The queried WBD data as a GeoDataFrame or DataFrame.
    """

    import geopandas as gpd
    import pandas as pd
    from shapely.geometry import Polygon

    def detect_geometry_type(geometry):
        """
        Automatically detect the geometry type based on the structure of the geometry dictionary.
        """
        if "x" in geometry and "y" in geometry:
            return "esriGeometryPoint"
        elif (
            "xmin" in geometry
            and "ymin" in geometry
            and "xmax" in geometry
            and "ymax" in geometry
        ):
            return "esriGeometryEnvelope"
        elif "rings" in geometry:
            return "esriGeometryPolygon"
        elif "paths" in geometry:
            return "esriGeometryPolyline"
        elif "points" in geometry:
            return "esriGeometryMultipoint"
        else:
            raise ValueError("Unsupported geometry type or invalid geometry structure.")

    allowed_digit_values = [2, 4, 6, 8, 10, 12, 14, 16]
    if digit not in allowed_digit_values:
        raise ValueError(
            f"Invalid digit value. Allowed values are {allowed_digit_values}"
        )

    layer = allowed_digit_values.index(digit) + 1

    # Convert GeoDataFrame to a dictionary if needed
    if isinstance(geometry, gpd.GeoDataFrame):
        geometry_dict = _convert_geodataframe_to_esri_format(geometry)[0]
        geometry_type = detect_geometry_type(geometry_dict)
    elif isinstance(geometry, dict):
        geometry_type = detect_geometry_type(geometry)
        geometry_dict = geometry
    elif isinstance(geometry, str):
        geometry_dict = geometry
    elif searchText is None:
        raise ValueError(
            "Invalid geometry input. Must be a GeoDataFrame or a dictionary."
        )
    else:
        geometry_dict = None

    if geometry_dict is not None:
        # Convert geometry to a JSON string (required by the API)
        if isinstance(geometry_dict, dict):
            geometry_json = json.dumps(geometry_dict)
        else:
            geometry_json = geometry_dict

        # Construct the query parameters
        params = {
            "geometry": geometry_json,
            "geometryType": geometry_type,
            "inSR": inSR,
            "spatialRel": spatialRel,
            "outFields": outFields,
            "returnGeometry": str(return_geometry).lower(),
            "f": "json",
        }
        # API URL for querying the WBD
        url = f"https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServer/{layer}/query"
    else:
        # Construct the query parameters
        params = {
            "searchText": searchText,
            "contains": "true",
            "layers": str(layer),
            "inSR": inSR,
            "outFields": outFields,
            "returnGeometry": str(return_geometry).lower(),
            "f": "json",
        }
        url = f"https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServer/find"

    # Add additional keyword arguments
    for key, value in kwargs.items():
        params[key] = value

    # Make the GET request
    response = requests.get(url, params=params)

    if response.status_code != 200:
        return {"error": f"Request failed with status code {response.status_code}"}

    data = response.json()

    if geometry_dict is not None:
        # Extract features from the API response
        features = data.get("features", [])
        crs = f"EPSG:{data['spatialReference']['latestWkid']}"
    else:
        features = data.get("results", [])
        crs = f"EPSG:{data['results'][0]['geometry']['spatialReference']['latestWkid']}"

    # Prepare attribute data and geometries
    attributes = [feature["attributes"] for feature in features]
    df = pd.DataFrame(attributes)
    df.rename(
        columns={"Shape__Length": "Shape_Length", "Shape__Area": "Shape_Area"},
        inplace=True,
    )

    # Handle geometries
    if return_geometry:
        geometries = [
            (
                Polygon(feature["geometry"]["rings"][0])
                if "rings" in feature["geometry"]
                else None
            )
            for feature in features
        ]
        gdf = gpd.GeoDataFrame(
            df,
            geometry=geometries,
            crs=crs,
        )
        if outSR != "3857":
            gdf = gdf.to_crs(outSR)

        if output is not None:
            gdf.to_file(output)

        return gdf
    else:
        return df


def get_nwi_by_huc8(
    huc8: Optional[str] = None,
    geometry: Optional[Union["gpd.GeoDataFrame", str]] = None,
    out_dir: Optional[str] = None,
    quiet: bool = True,
    layer: str = "Wetlands",
    **kwargs: Any,
) -> "gpd.GeoDataFrame":
    """
    Fetches National Wetlands Inventory (NWI) data by HUC8 code.

    Args:
        huc8 (Optional[str]): The HUC8 code to query the NWI data. It must be a
            string of length 8.
        geometry (Optional[Union[gpd.GeoDataFrame, str]]): The geometry to derive
            the HUC8 code. It can be a GeoDataFrame or a file path.
        out_dir (Optional[str]): The directory to save the downloaded data.
            Defaults to a temporary directory.
        quiet (bool): Whether to suppress download progress messages. Defaults to True.
        layer (str): The layer to fetch from the NWI data. It can be one of the following:
            Wetlands, Watershed, Riparian_Project_Metadata, Wetlands_Historic_Map_Info.
            Defaults to "Wetlands".
        **kwargs: Additional keyword arguments to pass to the download_file function.

    Returns:
        The fetched NWI data as a GeoDataFrame.

    Raises:
        ValueError: If the HUC8 code is invalid or the layer is not allowed.
    """
    import tempfile

    import geopandas as gpd

    if geometry is not None:
        wbd = get_wbd(geometry, return_geometry=False)
        huc8 = wbd["huc8"].values[0]

    if isinstance(huc8, str) and len(huc8) == 8:
        pass
    else:
        raise ValueError("Invalid HUC8 code. It must be a string of length 8.")

    if out_dir is None:
        out_dir = tempfile.gettempdir()

    allowed_layers = [
        "Wetlands",
        "Watershed",
        "Riparian_Project_Metadata",
        "Wetlands_Historic_Map_Info",
        "Wetlands_Project_Metadata",
    ]
    if layer not in allowed_layers:
        raise ValueError(f"Invalid layer. Allowed values are {allowed_layers}")

    url = f"https://documentst.ecosphere.fws.gov/wetlands/downloads/watershed/HU8_{huc8}_Watershed.zip"

    filename = os.path.join(out_dir, f"HU8_{huc8}_Watershed.zip")

    download_file(url, filename, quiet=quiet, **kwargs)

    data_dir = os.path.join(out_dir, f"HU8_{huc8}_Watershed")

    filepath = os.path.join(data_dir, f"HU8_{huc8}_{layer}.shp")

    gdf = gpd.read_file(filepath)
    return gdf


def read_geojson(data: str, **kwargs: Any) -> Dict[str, Any]:
    """
    Fetches and parses a GeoJSON file from a given URL.

    Args:
        data (str): The URL of the GeoJSON file.
        **kwargs (Any): Additional keyword arguments to pass to the requests.get() method.

    Returns:
        Dict[str, Any]: The parsed GeoJSON data.
    """

    return requests.get(data, **kwargs).json()


def get_max_pixel_coords(
    geotiff_path,
    band_idx=1,
    roi=None,
    dst_crs="EPSG:4326",
    output=None,
    return_gdf=True,
    **kwargs: Any,
):
    """
    Find the geographic coordinates of the maximum pixel value in a GeoTIFF.

    Args:
        geotiff_path (str): Path to the GeoTIFF file.
        band_idx (int): Band index to use (default is 1).
        roi (str): Path to a vector dataset containing the region of interest (default is None).
        dst_crs (str): Desired output coordinate system in EPSG format (e.g., "EPSG:4326").
        output (str): Path to save the output GeoDataFrame (default is None).
        return_gdf (bool): Whether to return a GeoDataFrame (default is True).

    Returns:
        Maximum pixel value and its geographic coordinates in the specified CRS.
    """
    import geopandas as gpd
    import numpy as np
    import rasterio
    from rasterio.mask import mask
    from rasterio.warp import transform, transform_geom

    with rasterio.open(geotiff_path) as dataset:
        # If ROI is provided, handle potential CRS differences
        if roi:
            if isinstance(roi, str):
                gdf = gpd.read_file(roi)
            elif isinstance(roi, gpd.GeoDataFrame):
                gdf = roi
            elif isinstance(roi, dict):
                gdf = gpd.GeoDataFrame.from_features([roi])
            else:
                raise ValueError(
                    "Invalid ROI input. Must be a file path or a GeoDataFrame."
                )
            roi_geojson = gdf.__geo_interface__

            # Reproject ROI to match the raster's CRS if necessary
            roi_crs = gdf.crs
            if roi_crs is None:
                roi_crs = "EPSG:4326"
            if roi_crs != dataset.crs.to_string():
                roi_geojson["features"][0]["geometry"] = transform_geom(
                    roi_crs,
                    dataset.crs.to_string(),
                    roi_geojson["features"][0]["geometry"],
                )

            # Mask the raster using the transformed ROI geometry
            clipped_band, clipped_transform = mask(
                dataset, [roi_geojson["features"][0]["geometry"]], crop=True
            )
            band = clipped_band[
                band_idx - 1
            ]  # Mask returns a 3D array (bands, rows, cols), so select the first band
            transform_to_use = clipped_transform
        else:
            # Use the entire raster
            band = dataset.read(band_idx)
            transform_to_use = dataset.transform

        # Find the maximum value and its index
        max_value = band.max()
        max_index = np.unravel_index(band.argmax(), band.shape)

        # Convert pixel coordinates to the raster's CRS coordinates
        original_coords = transform_to_use * (max_index[1], max_index[0])

        # Transform coordinates to the desired CRS
        src_crs = dataset.crs
        x, y = transform(src_crs, dst_crs, [original_coords[0]], [original_coords[1]])

        if return_gdf:
            x_coords = [x[0]]
            y_coords = [y[0]]
            # Create a DataFrame
            df = pd.DataFrame({"x": x_coords, "y": y_coords})

            # Convert the DataFrame to a GeoDataFrame
            gdf = gpd.GeoDataFrame(
                df, geometry=gpd.points_from_xy(df.x, df.y), crs=dst_crs
            )

            if output:
                gdf.to_file(output, **kwargs)

        else:
            return {"max_value": max_value, "coordinates": (x[0], y[0]), "crs": dst_crs}


def point_to_gdf(x, y, point_crs="EPSG:4326", to_crs="EPSG:4326", **kwargs: Any):
    """
    Convert a point to a GeoDataFrame.

    Args:
        x (float): X coordinate of the point.
        y (float): Y coordinate of the point.
        point_crs (str): Coordinate Reference System of the point.

    Returns:
        A GeoDataFrame containing the point.
    """
    import geopandas as gpd
    from shapely.geometry import Point

    # Create a Point object
    point = Point(x, y)

    # Convert the Point to a GeoDataFrame
    gdf = gpd.GeoDataFrame([{"geometry": point}], crs=point_crs)

    if to_crs != point_crs:
        gdf = gdf.to_crs(to_crs)

    return gdf


def download_nlcd(
    years: List[int], out_dir: str = None, quiet: bool = False, **kwargs: Any
) -> None:
    """
    Downloads NLCD (National Land Cover Database) files for the specified years.

    Args:
        years (List[int]): A list of years for which to download the NLCD files.
        out_dir (str, optional): The directory where the downloaded files will be saved.
            Defaults to the current working directory.
        quiet (bool, optional): If True, suppresses download progress messages. Defaults to False.
        **kwargs (Any): Additional keyword arguments to pass to the download_file function.

    Returns:
        None
    """

    allow_years = list(range(1985, 2024, 1))
    url = "https://s3-us-west-2.amazonaws.com/mrlc/Annual_NLCD_LndCov_{}_CU_C1V0.tif"
    if out_dir is None:
        out_dir = os.getcwd()
    elif not os.path.exists(out_dir):
        os.makedirs(out_dir)
    for year in years:
        if year not in allow_years:
            print(f"Year {year} is not available. Skipping...")
            continue
        year_url = url.format(year)
        basename = os.path.basename(year_url)
        filepath = os.path.join(out_dir, basename)
        download_file(year_url, filepath, quiet=quiet, **kwargs)


def connect_points_as_line(
    gdf: "GeoDataFrame",
    sort_column: Optional[str] = None,
    crs: str = "EPSG:4326",
    single_line: bool = True,
) -> "GeoDataFrame":
    """
    Connects points in a GeoDataFrame into either a single LineString or multiple LineStrings
    based on a specified sort column or the index if no column is provided. The resulting
    GeoDataFrame will have the specified CRS.

    Args:
        gdf (GeoDataFrame): A GeoDataFrame containing point geometries.
        sort_column (Optional[str]): Column name to sort the points by (e.g., 'timestamp').
                                     If None, the index is used for sorting. Defaults to None.
        crs (str): The coordinate reference system (CRS) for the resulting GeoDataFrame.
                   Defaults to "EPSG:4326".
        single_line (bool): If True, generates a single LineString connecting all points.
                            If False, generates multiple LineStrings, each connecting two consecutive points.
                            Defaults to True.

    Returns:
        A new GeoDataFrame containing either a single LineString or multiple LineString geometries.
                      based on the single_line parameter, with the specified CRS.

    Example:
        >>> line_gdf = connect_points_as_line(gdf, 'timestamp', crs="EPSG:3857", single_line=True)
        >>> line_gdf = connect_points_as_line(gdf, single_line=False)  # Uses index and defaults to EPSG:4326
    """
    import geopandas as gpd
    from shapely.geometry import LineString

    # Sort the GeoDataFrame by the specified column or by index if None
    gdf_sorted = gdf.sort_values(by=sort_column) if sort_column else gdf.sort_index()

    if single_line:
        # Create a single LineString connecting all points
        line = LineString(gdf_sorted.geometry.tolist())
        line_gdf = gpd.GeoDataFrame(geometry=[line], crs=crs)
    else:
        # Generate LineStrings for each consecutive pair of points
        lines = [
            LineString([gdf_sorted.geometry.iloc[i], gdf_sorted.geometry.iloc[i + 1]])
            for i in range(len(gdf_sorted) - 1)
        ]
        line_gdf = gpd.GeoDataFrame(geometry=lines, crs=crs)

    return line_gdf


def line_to_points(data: str) -> "GeoDataFrame":
    """
    Converts a LineString geometry in a GeoDataFrame into individual points.

    Args:
        data (str): A GeoDataFrame containing a LineString geometry.

    Returns:
        A new GeoDataFrame where each vertex of the LineString is a Point geometry.
    """
    import geopandas as gpd
    from geopandas import GeoDataFrame
    from shapely.geometry import LineString, Point

    if isinstance(data, str):
        line_gdf = gpd.read_file(data)
    elif isinstance(data, GeoDataFrame):
        line_gdf = data
    else:
        raise ValueError("Invalid input. Must be a file path or a GeoDataFrame.")

    # Ensure there is a LineString in the GeoDataFrame
    if not all(line_gdf.geometry.type == "LineString"):
        raise ValueError("Input GeoDataFrame must contain only LineString geometries.")

    # Extract the first (and only) LineString from the GeoDataFrame
    line = line_gdf.geometry.iloc[0]

    # Convert each point in the LineString to a Point geometry
    points = [Point(coord) for coord in line.coords]

    # Create a new GeoDataFrame with these points
    points_gdf = gpd.GeoDataFrame(geometry=points, crs=line_gdf.crs)

    return points_gdf


def evaluate_model(
    df: pd.DataFrame,
    y_col: str = "y",
    y_pred_col: str = "y_pred",
    metrics: list = None,
    drop_na: bool = True,
    filter_nonzero: bool = True,
) -> dict:
    """
    Evaluates the model performance on the given dataframe with customizable options.

    Args:
        df: A pandas DataFrame with columns for actual and predicted values.
        y_col: Column name for the actual values.
        y_pred_col: Column name for the predicted values.
        metrics: A list of metrics to calculate. Available options:
            - 'r2': R-squared
            - 'r': Pearson correlation coefficient
            - 'rmse': Root Mean Squared Error
            - 'mae': Mean Absolute Error
            - 'mape': Mean Absolute Percentage Error
            Defaults to all metrics if None.
        drop_na: Whether to drop rows with NaN in the actual values column.
        filter_nonzero: Whether to filter out rows where actual values are zero.

    Returns:
        A dictionary of the selected performance metrics.
    """

    import math

    try:
        from sklearn import metrics as skmetrics
    except ImportError:
        raise ImportError(
            "The scikit-learn package is required for this function. Install it using 'pip install scikit-learn'."
        )

    # Default metrics if none are provided
    if metrics is None:
        metrics = ["r2", "r", "rmse", "mae", "mape"]

    # Data preprocessing
    if drop_na:
        df = df.dropna(subset=[y_col])
    if filter_nonzero:
        df = df[df[y_col] != 0]

    # Metric calculations
    results = {}
    if "r2" in metrics:
        results["r2"] = skmetrics.r2_score(df[y_col], df[y_pred_col])
    if "r" in metrics:
        results["r"] = df[y_col].corr(df[y_pred_col])
    if "rmse" in metrics:
        results["rmse"] = math.sqrt(
            skmetrics.mean_squared_error(df[y_col], df[y_pred_col])
        )
    if "mae" in metrics:
        results["mae"] = skmetrics.mean_absolute_error(df[y_col], df[y_pred_col])
    if "mape" in metrics:
        results["mape"] = skmetrics.mean_absolute_percentage_error(
            df[y_col], df[y_pred_col]
        )

    return results


def plot_actual_vs_predicted(
    df: pd.DataFrame,
    x_col: str = "y",
    y_col: str = "y_pred",
    xlim: tuple = None,
    ylim: tuple = None,
    title: str = "Actual vs. Predicted Values",
    x_label: str = "Actual Values",
    y_label: str = "Predicted Values",
    marker_size: int = 6,
    marker_opacity: float = 0.7,
    marker_color: str = "blue",
    line_color: str = "red",
    line_dash: str = "dash",
    width: int = 800,
    height: int = 800,
    showlegend: bool = True,
    template: str = "plotly_white",
    square_aspect: bool = True,
    return_figure: bool = False,
    **kwargs: Any,
):
    """
    Plots a customizable scatter plot with a reference line for actual vs. predicted values.

    Args:
        df: A pandas DataFrame with columns for actual and predicted values.
        x_col: Column name for the x-axis (actual values).
        y_col: Column name for the y-axis (predicted values).
        xlim: A tuple (min, max) for x-axis limits.
        ylim: A tuple (min, max) for y-axis limits.
        title: Title of the plot.
        x_label: Label for the x-axis.
        y_label: Label for the y-axis.
        marker_size: Size of the scatter plot markers.
        marker_opacity: Opacity of the scatter plot markers.
        marker_color: Color of the scatter plot markers.
        line_color: Color of the reference line.
        line_dash: Dash style of the reference line ('dash', 'dot', etc.).
        width: Width of the plot in pixels.
        height: Height of the plot in pixels.
        showlegend: Whether to show the legend.
        template: Plotly template for styling.
        square_aspect: Whether to enforce a square aspect ratio.
        return_figure: Whether to return the Plotly figure object.
        **kwargs: Additional keyword arguments for Plotly figure.

    Returns:
        A Plotly figure object.

    """
    import plotly.graph_objects as go

    # Default x and y limits if not provided
    x_min, x_max = xlim if xlim else (df[x_col].min(), df[x_col].max())
    y_min, y_max = ylim if ylim else (df[y_col].min(), df[y_col].max())

    # Scatter plot for actual vs predicted
    scatter = go.Scatter(
        x=df[x_col],
        y=df[y_col],
        mode="markers",
        marker=dict(size=marker_size, opacity=marker_opacity, color=marker_color),
        name="Predicted vs Actual",
    )

    # Reference line y = x
    ref_line = go.Scatter(
        x=[x_min, x_max],
        y=[x_min, x_max],
        mode="lines",
        line=dict(color=line_color, dash=line_dash),
        name="Reference Line",
    )

    # Layout settings
    layout = go.Layout(
        title=title,
        xaxis=dict(title=x_label, range=[x_min, x_max]),
        yaxis=dict(title=y_label, range=[y_min, y_max]),
        template=template,
        showlegend=showlegend,
        height=height,
        width=width,
    )

    # Ensure square aspect ratio if specified
    if square_aspect:
        layout.update(xaxis_scaleanchor="y")

    # Create the figure
    fig = go.Figure(data=[scatter, ref_line], layout=layout, **kwargs)
    if return_figure:
        return fig
    else:
        fig.show()


def search_mapillary_images(
    lon: Optional[float] = None,
    lat: Optional[float] = None,
    radius: float = 0.00005,
    bbox: Optional[Union[str, List[float]]] = None,
    limit: int = 2000,
    access_token: Optional[str] = None,
) -> List[str]:
    """
    Retrieves Mapillary image IDs near the specified test point within a bounding box.

    Args:
        lon (float, optional): Longitude of the test point. Defaults to None.
        lat (float, optional): Latitude of the test point. Defaults to None.
        radius (float, optional): Radius to create the bounding box. Defaults to 0.00005.
        bbox (Union[str, List[float]], optional): Bounding box coordinates. Defaults to None.
        limit (int, optional): Maximum number of image IDs to retrieve. Defaults to 2000.
        access_token (str, optional): Mapillary API access token. Defaults to None.

    Returns:
        JSON response from the Mapillary API containing image IDs.
    """

    if access_token is None:
        access_token = get_api_key("MAPILLARY_API_KEY")

    if access_token is None:
        raise ValueError(
            "Mapillary API access token is required. Set it using the 'access_token' parameter."
        )

    metadata_endpoint = "https://graph.mapillary.com"
    headers = {"Authorization": f"OAuth {access_token}"}

    if bbox is None:
        if lon is None or lat is None:
            raise ValueError("Longitude and latitude are required.")
        bbox = f"{lon - radius},{lat - radius},{lon + radius},{lat + radius}"
    else:
        if isinstance(bbox, list):
            bbox = ",".join(str(x) for x in bbox)

    # Construct the bounding box for the API call
    url_imagesearch = f"{metadata_endpoint}/images?fields=id&bbox={bbox}&limit={limit}"

    try:
        response = requests.get(url_imagesearch, headers=headers)
        response.raise_for_status()  # Raise an HTTPError for bad responses
        return [image["id"] for image in response.json()["data"]]
    except requests.exceptions.RequestException as e:
        print(f"An error occurred: {e}")
        return []


def get_mapillary_image_widget(
    image_id: str,
    style: str = "photo",
    width: int = 800,
    height: int = 600,
    frame_border: int = 0,
    **kwargs: Any,
) -> widgets.HTML:
    """
    Creates an iframe widget to display a Mapillary image.

    Args:
        image_id (str): The ID of the Mapillary image.
        style (str): The style of the image. Can be "photo", "classic", "split". Defaults to "photo".
        width (int): The width of the iframe. Defaults to 800.
        height (int): The height of the iframe. Defaults to 600.
        frame_border (int): The frame border of the iframe. Defaults to 0.
        **kwargs: Additional keyword arguments for the widget.

    Returns:
        An iframe widget displaying the Mapillary image.
    """

    content = f"""
    <iframe
        src="https://www.mapillary.com/embed?image_key={image_id}&style={style}"
        height="{height}"
        width="{width}"
        frameborder="{frame_border}">
    </iframe>
    """

    # Create an iframe widget
    iframe = widgets.HTML(value=content, placeholder="Mapillary Image", **kwargs)

    return iframe


def get_mapillary_image_url(
    image_id: str,
    resolution: str = "original",
    access_token: Optional[str] = None,
    **kwargs: Any,
) -> Optional[str]:
    """
    Retrieves the URL of a Mapillary image.

    Args:
        image_id (str): The ID of the Mapillary image.
        resolution (str): The resolution of the image. Can be 256, 1024, 2048, or original.
            Defaults to "original".
        access_token (str, optional): The access token for the Mapillary API. Defaults to None.
        **kwargs: Additional keyword arguments for the request.

    Raises:
        ValueError: If no access token is provided.

    Returns:
        The URL of the Mapillary image, or None if an error occurs.
    """
    if access_token is None:
        access_token = get_api_key("MAPILLARY_API_KEY")

    if access_token is None:
        raise ValueError(
            "Mapillary API access token is required. Set it using the 'access_token' parameter."
        )

        # API URL
    url = f"https://graph.mapillary.com/{image_id}"

    # Fields to retrieve
    fields = f"thumb_{resolution}_url"

    # Request parameters
    params = {"fields": fields, "access_token": access_token}

    # Fetch the data
    response = requests.get(url, params=params, **kwargs)

    # Check the response
    if response.status_code == 200:
        data = response.json()
        image_url = data.get(fields)
        return image_url
    else:
        print(f"Error {response.status_code}: {response.text}")
        return None


def download_mapillary_image(
    image_id: str,
    output: Optional[str] = None,
    resolution: str = "original",
    access_token: Optional[str] = None,
    quiet: bool = True,
    **kwargs: Any,
) -> None:
    """
    Downloads a Mapillary image.

    Args:
        image_id (str): The ID of the Mapillary image.
        output (str, optional): The output file path. Defaults to None.
        resolution (str): The resolution of the image. Can be 256, 1024, 2048, or original.
            Defaults to "original".
        access_token (str, optional): The access token for the Mapillary API. Defaults to None.
        quiet (bool): Whether to suppress output. Defaults to True.
        **kwargs: Additional keyword arguments for the download.

    Returns:
        None
    """

    image_url = get_mapillary_image_url(
        image_id, resolution=resolution, access_token=access_token
    )
    if output is None:

        output = f"{image_id}.jpg"
    download_file(image_url, output, quiet=quiet, **kwargs)


def download_mapillary_images(
    image_ids: List[str],
    output_dir: Optional[str] = None,
    resolution: str = "original",
    **kwargs: Any,
) -> None:
    """
    Downloads multiple Mapillary images.

    Args:
        image_ids (List[str]): A list of Mapillary image IDs.
        output_dir (str, optional): The directory to save the images. Defaults
            to the current working directory.
        resolution (str): The resolution of the images. Defaults to "original".
        **kwargs: Additional keyword arguments for the download.

    Returns:
        None
    """
    if output_dir is None:
        output_dir = os.getcwd()

    for index, image_id in enumerate(image_ids):
        output = os.path.join(output_dir, f"{image_id}.jpg")
        print(f"Downloading {index + 1}/{len(image_ids)}: {image_id}.jpg ...")
        download_mapillary_image(
            image_id=image_id, output=output, resolution=resolution, **kwargs
        )


def get_image_colormap(image, index=1):
    """
    Retrieve the colormap from an image.

    Args:
        image (str, rasterio.io.DatasetReader, rioxarray.DataArray):
            The input image. It can be:
            - A file path to a raster image (string).
            - A rasterio dataset.
            - A rioxarray DataArray.
        index (int): The band index to retrieve the colormap from (default is 1).

    Returns:
        A dictionary representing the colormap (value: (R, G, B, A)), or None if no colormap is found.

    Raises:
        ValueError: If the input image type is unsupported.
    """
    import rasterio
    import rioxarray
    import xarray as xr

    def remove_black_fills(d: dict) -> dict:
        """Remove keys with value (0, 0, 0, 255)."""
        return {str(k): v for k, v in d.items() if v != (0, 0, 0, 255)}

    dataset = None

    if isinstance(image, str):  # File path
        with rasterio.open(image) as ds:
            try:
                return remove_black_fills(ds.colormap(index)) if ds.count > 0 else None
            except:
                return None
    elif isinstance(image, rasterio.io.DatasetReader):  # rasterio dataset
        dataset = image
    elif isinstance(image, xr.DataArray) or isinstance(image, xr.Dataset):
        source = image.encoding.get("source")
        if source:
            with rasterio.open(source) as ds:
                return remove_black_fills(ds.colormap(index)) if ds.count > 0 else None
        else:
            raise ValueError(
                "Cannot extract colormap: DataArray does not have a source."
            )
    else:
        raise ValueError(
            "Unsupported input type. Provide a file path, rasterio dataset, or rioxarray DataArray."
        )

    if dataset:
        return (
            remove_black_fills(dataset.colormap(index)) if dataset.count > 0 else None
        )


def write_image_colormap(image, colormap, output_path=None):
    """
    Apply or update a colormap to a raster image.

    Args:
        image (str, rasterio.io.DatasetReader, rioxarray.DataArray):
            The input image. It can be:
            - A file path to a raster image (string).
            - A rasterio dataset.
            - A rioxarray DataArray.
        colormap (dict): A dictionary defining the colormap (value: (R, G, B, A)).
        output_path (str, optional): Path to save the updated raster image.
            If None, the original file is updated in-memory.

    Returns:
        Path to the updated raster image.

    Raises:
        ValueError: If the input image type is unsupported.
    """
    import rasterio
    import rioxarray
    import xarray as xr

    dataset = None
    src_profile = None
    src_data = None

    if isinstance(image, str):  # File path
        with rasterio.open(image) as ds:
            dataset = ds
            src_profile = ds.profile
            src_data = ds.read(1)  # Assuming single-band
    elif isinstance(image, rasterio.io.DatasetReader):  # rasterio dataset
        dataset = image
        src_profile = dataset.profile
        src_data = dataset.read(1)  # Assuming single-band
    elif isinstance(image, xr.DataArray):  # rioxarray DataArray
        source = image.encoding.get("source")
        if source:
            with rasterio.open(source) as ds:
                dataset = ds
                src_profile = ds.profile
                src_data = ds.read(1)  # Assuming single-band
        else:
            raise ValueError("Cannot apply colormap: DataArray does not have a source.")
    else:
        raise ValueError(
            "Unsupported input type. Provide a file path, rasterio dataset, or rioxarray DataArray."
        )

    # Ensure the dataset is single-band
    if dataset.count != 1:
        raise ValueError(
            "Colormaps can only be applied to single-band raster datasets."
        )

    # Update the profile and colormap
    src_profile.update(dtype=src_data.dtype, count=1)

    if not output_path:
        output_path = "output_with_colormap.tif"

    # Check and sanitize colormap
    fixed_colormap = {}
    for k, v in colormap.items():
        if not isinstance(k, int):
            k = int(k)
        if len(v) == 3:  # RGB
            fixed_colormap[k] = tuple(int(c) for c in v)
        elif len(v) == 4:  # RGBA
            fixed_colormap[k] = tuple(
                int(c) for c in v[:3]
            )  # Drop alpha for compatibility
        else:
            raise ValueError(f"Invalid colormap value: {v}")

    # Write the updated dataset with the colormap
    with rasterio.open(output_path, "w", **src_profile) as dst:
        dst.write(src_data, 1)
        dst.write_colormap(1, fixed_colormap)

    return output_path


def color_code_dataframe(
    data: Union[str, pd.DataFrame, "gpd.GeoDataFrame"], legend_dict: Dict[str, str]
) -> Union[pd.DataFrame, "gpd.GeoDataFrame"]:
    """Converts values in a dataframe to color codes based on a legend dictionary.

    This function takes a dataframe (or path to a dataframe) and a legend dictionary
    and returns a new dataframe with values replaced by their corresponding color codes.
    It supports both numeric range legends and categorical legends.

    Args:
        data: Input data source, can be:
            - Path to a CSV file or geospatial file
            - pandas DataFrame
            - geopandas GeoDataFrame
        legend_dict: Dictionary mapping values to colors, can be:
            - Numeric ranges ("[ 100000, 200000]") mapped to color codes
            - Categorical values ("low", "medium") mapped to color codes
            - Can include a "Nodata" key for None/NaN values

    Returns:
        A new dataframe with values replaced by color codes, preserving the
        input data type (DataFrame or GeoDataFrame)

    Raises:
        TypeError: If the input data type is not supported
        ValueError: If the file format is not supported

    Examples:
        >>> # Example with numeric ranges
        >>> range_legend = {
        ...     "[ 0, 200000]": "#daeaf6",
        ...     "(200001, 400000]": "#9ecae1",
        ...     "Nodata": "#f0f0f0"
        ... }
        >>> color_df = color_code_dataframe("housing_data.csv", range_legend)

        >>> # Example with categorical values
        >>> cat_legend = {
        ...     "low": "#daeaf6",
        ...     "medium": "#9ecae1",
        ...     "high": "#2171b5",
        ...     "Nodata": "#f0f0f0"
        ... }
        >>> df = pd.DataFrame({"Risk": ["low", "medium", "high", None]})
        >>> color_df = color_code_dataframe(df, cat_legend)
    """
    import re

    # Handle different input types
    if isinstance(data, str):
        # Input is a path to a file
        path = Path(data)
        if path.suffix.lower() == ".csv":
            df = pd.read_csv(data)
        elif path.suffix.lower() in [".geojson", ".shp"]:
            df = gpd.read_file(data)
        else:
            raise ValueError(f"Unsupported file format: {path.suffix}")
    elif isinstance(data, pd.DataFrame):
        # Input is already a pandas DataFrame
        df = data.copy()
    elif isinstance(data, gpd.GeoDataFrame):
        # Input is a GeoDataFrame
        df = data.copy()
    else:
        raise TypeError("Input must be a file path, pandas DataFrame, or GeoDataFrame")

    # Determine legend type: numeric ranges or categorical
    # Exclude "Nodata" key from this check
    legend_without_nodata = {
        k: v
        for k, v in legend_dict.items()
        if k != "Nodata" and not isinstance(k, str) or k.lower() != "nodata"
    }
    is_range_legend = any(
        "[" in key or "(" in key for key in legend_without_nodata.keys()
    )

    # Get the "Nodata" color if provided
    nodata_keys = ["Nodata", "nodata", "NODATA", "NoData"]
    nodata_color = None
    for key in nodata_keys:
        if key in legend_dict:
            nodata_color = legend_dict[key]
            break

    # Function to get color based on numeric value and range legend
    def get_color_for_numeric(value: Any) -> Optional[str]:
        """Maps a numeric value to a color based on the range legend.

        Args:
            value: The value to map to a color

        Returns:
            The corresponding color code or None if no match is found
        """
        if pd.isna(value) or value is None:
            return nodata_color

        if not isinstance(value, (int, float)):
            return nodata_color

        for range_str, color in legend_dict.items():
            # Skip the Nodata entry
            if isinstance(range_str, str) and range_str.lower() == "nodata":
                continue

            # Parse the range string like "[ 182913, 357522]" or "( 357522, 415584]"
            match = re.search(r"[\[\(]\s*(\d+),\s*(\d+)[\]\)]", range_str)
            if not match:
                continue

            lower_bound = int(match.group(1))
            upper_bound = int(match.group(2))
            lower_inclusive = range_str.startswith("[")
            upper_inclusive = range_str.endswith("]")

            # Check if the value is within the range
            above_lower = (
                value >= lower_bound if lower_inclusive else value > lower_bound
            )
            below_upper = (
                value <= upper_bound if upper_inclusive else value < upper_bound
            )

            if above_lower and below_upper:
                return color

        return None

    # Function to get color based on categorical value
    def get_color_for_categorical(value: Any) -> Optional[str]:
        """Maps a categorical value to a color.

        Args:
            value: The value to map to a color

        Returns:
            The corresponding color code or None if no match is found
        """
        if pd.isna(value) or value is None:
            return nodata_color

        # Convert to string for comparison
        str_value = str(value).lower()

        # Try direct matching
        if str_value in legend_dict:
            return legend_dict[str_value]

        # Try case-insensitive matching
        for cat, color in legend_dict.items():
            if isinstance(cat, str) and cat.lower() == "nodata":
                continue

            if isinstance(cat, str) and cat.lower() == str_value:
                return color

        return None

    # Select appropriate color mapping function
    get_color = get_color_for_numeric if is_range_legend else get_color_for_categorical

    # Identify columns to process
    if is_range_legend:
        # For numeric ranges, look for numeric columns and date-formatted columns
        columns_to_process: List[str] = []
        for col in df.columns:
            # Check if column name matches date pattern
            if isinstance(col, str) and re.match(r"^\d{4}-\d{2}-\d{2}$", col):
                columns_to_process.append(col)
                continue

            # Check if column contains numbers
            if pd.api.types.is_numeric_dtype(df[col]):
                # Exclude columns that are likely IDs or categorical codes
                if not (
                    col.lower().endswith("id")
                    or "code" in col.lower()
                    or "fips" in col.lower()
                ):
                    columns_to_process.append(col)
    else:
        # For categorical legend, look for object or category columns
        columns_to_process: List[str] = []
        for col in df.columns:
            if pd.api.types.is_object_dtype(
                df[col]
            ) or pd.api.types.is_categorical_dtype(df[col]):
                columns_to_process.append(col)
            # Also check if numeric columns might contain discrete categories
            elif pd.api.types.is_numeric_dtype(df[col]) and df[col].nunique() < 10:
                columns_to_process.append(col)

    # Replace each value with its corresponding color
    for col in columns_to_process:
        df[col] = df[col].apply(get_color)

    return df


def get_overture_latest_release(patch=False) -> str:
    """
    Retrieves the value of the 'latest' key from the Overture Maps release JSON file.

    Args:
        patch (bool): If True, returns the full version string (e.g., "2025-02-19.0").

    Returns:
        The value of the 'latest' key from the releases.json file.

    Raises:
        requests.RequestException: If there's an issue with the HTTP request.
        KeyError: If the 'latest' key is not found in the JSON data.
        json.JSONDecodeError: If the response cannot be parsed as JSON.
    """
    url = "https://labs.overturemaps.org/data/releases.json"

    try:
        response = requests.get(url)
        response.raise_for_status()  # Raise an exception for HTTP errors

        data = response.json()
        if patch:
            latest_release = data.get("latest")
        else:
            latest_release = data.get("latest").split(".")[
                0
            ]  # Extract the version number

        if latest_release is None:
            raise KeyError("The 'latest' key was not found in the releases.json file")

        return latest_release

    except requests.RequestException as e:
        print(f"Error making the request: {e}")
        raise
    except json.JSONDecodeError as e:
        print(f"Error parsing JSON response: {e}")
        raise
    except KeyError as e:
        print(f"Key error: {e}")
        raise


def set_proj_lib_path(verbose=False):
    """
    Set the PROJ_LIB and GDAL_DATA environment variables based on the current conda environment.

    This function attempts to locate and set the correct paths for PROJ_LIB and GDAL_DATA
    by checking multiple possible locations within the conda environment structure.

    Args:
        verbose (bool): If True, print additional information during the process.

    Returns:
        True if both paths were set successfully, False otherwise.
    """
    import sys

    try:
        from rasterio.env import set_gdal_config

        # Get conda environment path
        conda_env_path = os.environ.get("CONDA_PREFIX") or sys.prefix

        # Define possible paths for PROJ_LIB
        possible_proj_paths = [
            os.path.join(conda_env_path, "share", "proj"),
            os.path.join(conda_env_path, "Library", "share", "proj"),
            os.path.join(conda_env_path, "Library", "share"),
        ]

        # Define possible paths for GDAL_DATA
        possible_gdal_paths = [
            os.path.join(conda_env_path, "share", "gdal"),
            os.path.join(conda_env_path, "Library", "share", "gdal"),
            os.path.join(conda_env_path, "Library", "data", "gdal"),
            os.path.join(conda_env_path, "Library", "share"),
        ]

        # Set PROJ_LIB environment variable
        proj_set = False
        for proj_path in possible_proj_paths:
            if os.path.exists(proj_path) and os.path.isdir(proj_path):
                # Verify it contains projection data
                if os.path.exists(os.path.join(proj_path, "proj.db")):
                    os.environ["PROJ_LIB"] = proj_path
                    if verbose:
                        print(f"PROJ_LIB set to: {proj_path}")
                    proj_set = True
                    break

        # Set GDAL_DATA environment variable
        gdal_set = False
        for gdal_path in possible_gdal_paths:
            if os.path.exists(gdal_path) and os.path.isdir(gdal_path):
                # Verify it contains the header.dxf file or other critical GDAL files
                if os.path.exists(
                    os.path.join(gdal_path, "header.dxf")
                ) or os.path.exists(os.path.join(gdal_path, "gcs.csv")):
                    os.environ["GDAL_DATA"] = gdal_path
                    if verbose:
                        print(f"GDAL_DATA set to: {gdal_path}")
                    gdal_set = True
                    break

        # If paths still not found, try a last-resort approach
        if not proj_set or not gdal_set:
            # Try a deep search in the conda environment
            for root, dirs, files in os.walk(conda_env_path):
                if not gdal_set and "header.dxf" in files:
                    os.environ["GDAL_DATA"] = root
                    if verbose:
                        print(f"GDAL_DATA set to: {root} (deep search)")
                    gdal_set = True

                if not proj_set and "proj.db" in files:
                    os.environ["PROJ_LIB"] = root
                    if verbose:
                        print(f"PROJ_LIB set to: {root} (deep search)")
                    proj_set = True

                if proj_set and gdal_set:
                    break

        set_gdal_config("PROJ_LIB", os.environ["PROJ_LIB"])
        set_gdal_config("GDAL_DATA", os.environ["GDAL_DATA"])

    except Exception as e:
        print(f"Error setting projection library paths: {e}")
        return


def read_vector(source, layer=None, **kwargs: Any):
    """Reads vector data from various formats including GeoParquet.

    This function dynamically determines the file type based on extension
    and reads it into a GeoDataFrame. It supports both local files and HTTP/HTTPS URLs.

    Args:
        source (str): Path to the vector file or URL.
        layer (str | int, optional): String or integer specifying which layer to read from multi-layer
            files (only applicable for formats like GPKG, GeoJSON, etc.).
            Defaults to None.
        **kwargs (Any): Additional keyword arguments to pass to the underlying reader.

    Returns:
        A GeoDataFrame containing the vector data.

    Raises:
        ValueError: If the file format is not supported or source cannot be accessed.

    Examples:
        Read a local shapefile
        >>> gdf = read_vector("path/to/data.shp")
        >>>
        Read a GeoParquet file from URL
        >>> gdf = read_vector("https://example.com/data.parquet")
        >>>
        Read a specific layer from a GeoPackage
        >>> gdf = read_vector("path/to/data.gpkg", layer="layer_name")
    """

    import urllib.parse

    import fiona
    import geopandas as gpd

    # Determine if source is a URL or local file
    parsed_url = urllib.parse.urlparse(source)
    is_url = parsed_url.scheme in ["http", "https"]

    # If it's a local file, check if it exists
    if not is_url and not os.path.exists(source):
        raise ValueError(f"File does not exist: {source}")
    elif is_url and source.endswith(".parquet"):
        source = download_file(source, quiet=True, overwrite=True)

    # Get file extension
    _, ext = os.path.splitext(source)
    ext = ext.lower()

    # Handle GeoParquet files
    if ext in [".parquet", ".pq", ".geoparquet"]:
        return read_parquet(source, **kwargs)

    # Handle common vector formats
    if ext in [".shp", ".geojson", ".json", ".gpkg", ".gml", ".kml", ".gpx"]:
        # For formats that might have multiple layers
        if ext in [".gpkg", ".gml"] and layer is not None:
            return gpd.read_file(source, layer=layer, **kwargs)
        return gpd.read_file(source, **kwargs)

    # Try to use fiona to identify valid layers for formats that might have them
    # Only attempt this for local files as fiona.listlayers might not work with URLs
    if layer is None and ext in [".gpkg", ".gml"] and not is_url:
        try:
            layers = fiona.listlayers(source)
            if layers:
                return gpd.read_file(source, layer=layers[0], **kwargs)
        except Exception:
            # If listing layers fails, we'll fall through to the generic read attempt
            pass

    # For other formats or when layer listing fails, attempt to read using GeoPandas
    try:
        return gpd.read_file(source, **kwargs)
    except Exception as e:
        raise ValueError(f"Could not read from source '{source}': {str(e)}")


def json_to_geojson(input_path: str, output_path: str) -> None:
    """
    Converts a JSON file to a GeoJSON file.

    Args:
        input_path (str): The file path to the input JSON file.
        output_path (str): The file path to save the output GeoJSON file.

    Raises:
        FileNotFoundError: If the input file does not exist.
        ValueError: If the input JSON file is not properly formatted.
    """
    # Load the input JSON file
    with open(input_path, "r") as f:
        data = json.load(f)

    # Wrap each item as a GeoJSON feature
    features = []
    for item in data:
        feature = {
            "type": "Feature",
            "geometry": item.get("geometry"),
            "properties": item.get("properties", {}),
            "id": item.get("id"),
        }
        features.append(feature)

    # Build the GeoJSON FeatureCollection
    geojson = {"type": "FeatureCollection", "features": features}

    # Save the GeoJSON file
    with open(output_path, "w") as f:
        json.dump(geojson, f, indent=2)


def get_unique_name(name: str, names: list, overwrite: bool = False) -> str:
    """
    Generates a unique name based on the input name and existing names.

    Args:
        name (str): The base name to generate a unique name from.
        names (list): A list of existing names to check against.
        overwrite (bool, optional): If True, the function will return the original name even if it exists in the list. Defaults to False.

    Returns:
        A unique name based on the input name.
    """
    if overwrite or name not in names:
        return name
    else:
        counter = 1
        while True:
            unique_name = f"{name}_{counter}"
            if unique_name not in names:
                return unique_name
            counter += 1


def filter_geom_type(
    data: Union[str, dict, "gpd.GeoDataFrame"],
    geom_type: str,
    output: Optional[str] = None,
    **kwargs: Any,
) -> "gpd.GeoDataFrame":
    """
    Filters a GeoDataFrame based on the geometry type.

    Args:
        data (Union[str, dict, gpd.GeoDataFrame]): The GeoDataFrame to filter.
        geom_type (str): The geometry type to filter by.
        output (Optional[str], optional): The file path to save the filtered GeoDataFrame. Defaults to None.
        **kwargs: Additional keyword arguments to pass to the GeoDataFrame.read_file method.

    Returns:
        The filtered GeoDataFrame.
    """

    if isinstance(data, str):
        data = read_vector(data)
    elif isinstance(data, dict):
        data = gpd.GeoDataFrame.from_features(data)
    elif isinstance(data, gpd.GeoDataFrame):
        pass
    else:
        raise ValueError(
            "Invalid data type. Must be a string, dictionary, or GeoDataFrame."
        )

    filtered = data[data.geom_type == geom_type]

    if output is not None:
        if os.path.exists(output):
            if len(filtered) < len(data):
                filtered.to_file(output, **kwargs)
        else:
            filtered.to_file(output, **kwargs)

    return filtered


def create_lines_from_points(
    src_points: Union[dict, str, "gpd.GeoDataFrame"],
    dst_points: Union[dict, str, "gpd.GeoDataFrame"],
    col: str = "id",
    distance_col: str = "distance",
    decimal_places: int = 2,
    return_gdf: bool = False,
) -> dict:
    """
    Create LineString features between matching point features from two GeoJSON FeatureCollections
    based on a shared column (default is 'id').

    Parameters:
        src_points (Union[dict, str, gpd.GeoDataFrame]): Source GeoJSON FeatureCollection with point features.
        dst_points (Union[dict, str, gpd.GeoDataFrame]): Destination GeoJSON FeatureCollection with point features.
        col (str): The property name to match features between the two collections.
        distance_col (str): The name of the column to store the distance between the points.
        decimal_places (int): The number of decimal places to round the distance to.
        return_gdf (bool): If True, returns a GeoDataFrame instead of a dictionary. Defaults to False.

    Returns:
        A GeoJSON FeatureCollection containing LineString features.
    """
    import geopandas as gpd

    # Convert inputs to GeoJSON FeatureCollections if necessary
    if isinstance(src_points, str):
        src_points = read_vector(src_points).__geo_interface__
    elif isinstance(src_points, gpd.GeoDataFrame):
        src_points = src_points.__geo_interface__

    if isinstance(dst_points, str):
        dst_points = read_vector(dst_points).__geo_interface__
    elif isinstance(dst_points, gpd.GeoDataFrame):
        dst_points = dst_points.__geo_interface__

    # Build a lookup from col value to coordinates in dst_points
    dst_lookup = {
        feature[col] if col in feature else feature["properties"][col]: feature[
            "geometry"
        ]["coordinates"]
        for feature in dst_points["features"]
    }

    lines = []
    for feature in src_points["features"]:
        match_value = feature[col] if col in feature else feature["properties"][col]
        coords1 = feature["geometry"]["coordinates"]
        coords2 = dst_lookup.get(match_value)

        if coords2:
            line = {
                "type": "Feature",
                "geometry": {"type": "LineString", "coordinates": [coords1, coords2]},
                "properties": {col: match_value, **feature.get("properties", {})},
            }
            lines.append(line)

    gdf = gpd.GeoDataFrame.from_features(lines, crs="EPSG:4326")
    if distance_col:
        gdf_proj = gdf.to_crs("EPSG:3857")
        gdf[distance_col] = gdf_proj.length.round(decimal_places)

    if return_gdf:
        return gdf
    else:
        return gdf.__geo_interface__


def s3_to_https(s3_url: str, region: str = "af-south-1") -> str:
    """
    Convert an S3 URI to an HTTPS URL for public access.

    Parameters:
        s3_url (str): The S3 URI, e.g., "s3://bucket-name/path/to/file.tif"
        region (str): The AWS region where the bucket is located.

    Returns:
        The corresponding HTTPS URL.

    Examples:
        >>> s3_url = "s3://deafrica-services/crop_mask/northern/x187/y109/2019--P1Y/crop_mask_x187y109_2019--P1Y_mask.tif"
        >>> https_url = s3_to_https(s3_url, region="af-south-1")
        >>> print(https_url)
        # https://deafrica-services.s3.af-south-1.amazonaws.com/crop_mask/northern/x187/y109/2019--P1Y/crop_mask_x187y109_2019--P1Y_mask.tif
    """
    if not s3_url.startswith("s3://"):
        raise ValueError("Input must be an S3 URI starting with 's3://'")

    s3_path = s3_url.replace("s3://", "")
    parts = s3_path.split("/", 1)
    if len(parts) != 2:
        raise ValueError("Invalid S3 URI format")

    bucket, key = parts
    https_url = f"https://{bucket}.s3.{region}.amazonaws.com/{key}"
    return https_url


class GoogleMapsTileProvider(xyzservices.TileProvider):
    """Google Maps TileProvider."""

    MAP_TYPE_CONFIG = {
        "roadmap": {"mapType": "roadmap"},
        "satellite": {"mapType": "satellite"},
        "terrain": {
            "mapType": "terrain",
            "layerTypes": ["layerRoadmap"],
        },
        "hybrid": {
            "mapType": "satellite",
            "layerTypes": ["layerRoadmap"],
        },
    }

    def __init__(
        self,
        map_type: str = "roadmap",
        language: str = "en-Us",
        region: str = "US",
        api_key: Optional[str] = None,
        **kwargs: Any,
    ):
        """
        Generates Google Map tiles using the provided parameters. To get an API key
            and enable Map Tiles API, visit
            https://developers.google.com/maps/get-started#create-project.
            You can set the API key using the environment variable
            `GOOGLE_MAPS_API_KEY` or by passing it as an argument.

        Args:
            map_type (str, optional): The type of map to generate. Options are
                'roadmap', 'satellite', 'terrain', 'hybrid', 'traffic', 'streetview'.
                Defaults to 'roadmap'.
            language (str, optional): An IETF language tag that specifies the
                language used to display information on the tiles, such as 'zh-Cn'.
                Defaults to 'en-Us'.
            region (str, optional): A Common Locale Data Repository region
                identifier (two uppercase letters) that represents the physical
                location of the user. Defaults to 'US'.
            api_key (str, optional): The API key to use for the Google Maps API.
                If not provided, it will try to get it from the environment or
                Colab user data with the key 'GOOGLE_MAPS_API_KEY'. Defaults to None.
            **kwargs (Any): Additional parameters to pass to the map generation. For more
                info, visit https://bit.ly/3UhbZKU

        Raises:
            ValueError: If the API key is not provided and cannot be found in the
                environment or Colab user data.
            ValueError: If the map_type is not one of the allowed types.

        Example:
            >>> from leafmap.basemaps import GoogleMapsTileProvider
            >>> m = leafmap.Map()
            >>> basemap = GoogleMapsTileProvider(map_type='roadmap',
                language="en-Us", region="US", scale="scaleFactor2x", highDpi=True)
            >>> m.add_basemap(basemap)
        """

        key = api_key or get_google_maps_api_key()
        if key is None:
            raise ValueError(
                "API key is required to access Google Maps API. To get an API "
                "key and enable Map Tiles API, visit "
                "https://developers.google.com/maps/get-started#create-project"
            )

        if map_type not in self.MAP_TYPE_CONFIG:
            raise ValueError(f"map_type must be one of: {self.MAP_TYPE_CONFIG.keys()}")

        request_url = f"https://tile.googleapis.com/v1/createSession?key={key}"
        response = requests.post(
            url=request_url,
            headers={"Content-Type": "application/json"},
            json={
                **self.MAP_TYPE_CONFIG[map_type],
                "language": language,
                "region": region,
                **kwargs,
            },
            timeout=3,
        )

        if response.status_code == requests.codes.ok:
            json = response.json()
            map_name = map_type.capitalize()
            super().__init__(
                {
                    "url": f"https://tile.googleapis.com/v1/2dtiles/{{z}}/{{x}}/{{y}}?session={json['session']}&key={{accessToken}}",
                    "attribution": f"© Google {map_name}",
                    "accessToken": key,
                    "name": f"Google.{map_name}",
                    "ext": json["imageFormat"],
                    "tileSize": json["tileWidth"],
                }
            )
            self["url"] = self.build_url()
        else:
            raise RuntimeError(
                f"Error creating a Maps API session:\n{response.json()}."
            )


def get_google_map_tile_providers(
    language: str = "en-Us",
    region: str = "US",
    api_key: Optional[str] = None,
    **kwargs: Any,
) -> Dict[str, Any]:
    """
    Generates a dictionary of Google Map tile providers for different map types.

    Args:
        language (str, optional): An IETF language tag that specifies the
            language used to display information on the tiles, such as 'zh-Cn'.
            Defaults to 'en-Us'.
        region (str, optional): A Common Locale Data Repository region
            identifier (two uppercase letters) that represents the physical
            location of the user. Defaults to 'US'.
        api_key (str, optional): The API key to use for the Google Maps API.
            If not provided, it will try to get it from the environment or
            Colab user data with the key 'GOOGLE_MAPS_API_KEY'. Defaults to None.
        **kwargs: Additional parameters to pass to the map generation. For more
            info, visit https://bit.ly/3UhbZKU

    Returns:
        A dictionary where the keys are the map types and the values are tile providers.
        ('roadmap', 'satellite', 'terrain', 'hybrid')
        and the values are the corresponding GoogleMapsTileProvider objects.
    """
    gmap_providers = {}

    for m_type in GoogleMapsTileProvider.MAP_TYPE_CONFIG:
        gmap_providers[m_type] = GoogleMapsTileProvider(
            map_type=m_type, language=language, region=region, api_key=api_key, **kwargs
        )

    return gmap_providers


def get_google_maps_api_key(key: str = "GOOGLE_MAPS_API_KEY") -> Optional[str]:
    """
    Retrieves the Google Maps API key from the environment or Colab user data.

    Args:
        key (str, optional): The name of the environment variable or Colab user
            data key where the API key is stored. Defaults to
            'GOOGLE_MAPS_API_KEY'.

    Returns:
        The API key, or None if it could not be found.
    """
    if api_key := get_env_var(key):
        return api_key
    return os.environ.get(key, None)


def get_env_var(key: str) -> Optional[str]:
    """Retrieves an environment variable or Colab secret for the given key.

    Colab secrets have precedence over environment variables.

    Args:
        key (str): The key that's used to fetch the environment variable.

    Returns:
        The retrieved key, or None if no environment variable was found.
    """
    if not key:
        return None

    if "google.colab" in sys.modules:
        from google.colab import userdata

        try:
            return userdata.get(key)
        except (userdata.SecretNotFoundError, userdata.NotebookAccessError):
            pass

    return os.environ.get(key)


def get_nwi_year(
    xy: Optional[tuple] = None,
    bbox: Optional[list] = None,
    output: Optional[str] = None,
    fields: str = "*",
    epsg: int = 4326,
    return_geometry: bool = True,
):
    """
    Get the NWI year from the NWI map service.

    Args:
        xy: A tuple of (x, y) coordinates.
        bbox: A list of [xmin, ymin, xmax, ymax] coordinates.
        output: The file path to save the output GeoDataFrame.
        fields: The fields to return.
        epsg: The EPSG code of the coordinate system.
        return_geometry: Whether to return the geometry.
    """
    import geopandas as gpd

    if xy is not None:
        bbox = [xy[0], xy[1], xy[0], xy[1]]
    if bbox is not None:
        if len(bbox) == 2:
            bbox = [bbox[0], bbox[1], bbox[0], bbox[1]]
        if len(bbox) != 4:
            raise ValueError("bbox must be a list of 4 numbers")
        if not all(isinstance(x, (int, float)) for x in bbox):
            raise ValueError("bbox must be a list of 4 numbers")

    url = "https://fwspublicservices.wim.usgs.gov/wetlandsmapservice/rest/services/Data_Source/MapServer/3/query"
    params = {
        "f": "geojson",
        "where": "1=1",
        "geometry": f"{bbox[0]},{bbox[1]},{bbox[2]},{bbox[3]}",
        "geometryType": "esriGeometryEnvelope",
        "inSR": epsg,
        "spatialRel": "esriSpatialRelIntersects",
        "outFields": fields,
        "returnGeometry": return_geometry,
    }
    r = requests.get(url, params=params)
    if r.status_code == 200:
        gdf = gpd.GeoDataFrame.from_features(r.json())
        gdf.crs = f"EPSG:{epsg}"
        if output is not None:
            gdf.to_file(output)

        if return_geometry:
            return gdf
        else:
            return gdf["IMAGE_YR"].unique().tolist()
    else:
        print("Failed to download data:", r.status_code)
        return None


def clip_vector(input_gdf, clip_geom=None, bbox=None, output=None) -> Any:
    """
    Clip a vector dataset using either a bounding box or another vector dataset.

    Args:
        input_gdf (str | Path | gpd.GeoDataFrame): The input vector data, either as a file path or a GeoDataFrame.
        clip_geom (str | Path | gpd.GeoDataFrame, optional): A vector dataset used for clipping, either as a file path or GeoDataFrame.
        bbox (tuple, optional): Bounding box defined as (minx, miny, maxx, maxy).
        output (str | Path, optional): File path to save the clipped result. If None, the result is not saved.

    Returns:
        The clipped GeoDataFrame.

    Raises:
        ValueError: If both `clip_geom` and `bbox` are provided or neither is provided.
        ValueError: If `bbox` is not a 4-element tuple or list.
    """
    from pathlib import Path

    import geopandas as gpd
    from shapely.geometry import box

    # Load input_gdf if it's a file path
    if isinstance(input_gdf, (str, Path)):
        input_gdf = gpd.read_file(input_gdf)

    # Load clip_geom if it's a file path
    if isinstance(clip_geom, (str, Path)):
        clip_geom = gpd.read_file(clip_geom)

    if clip_geom is not None and bbox is not None:
        raise ValueError("Specify either 'clip_geom' or 'bbox', not both.")

    if clip_geom is not None:
        if input_gdf.crs != clip_geom.crs:
            clip_geom = clip_geom.to_crs(input_gdf.crs)
        clipped = gpd.clip(input_gdf, clip_geom)

    elif bbox is not None:
        if not isinstance(bbox, (tuple, list)) or len(bbox) != 4:
            raise ValueError("bbox must be a tuple or list of (minx, miny, maxx, maxy)")
        minx, miny, maxx, maxy = bbox
        bbox_geom = gpd.GeoDataFrame(
            geometry=[box(minx, miny, maxx, maxy)], crs="EPSG:4326"
        )
        bbox_geom = bbox_geom.to_crs(input_gdf.crs)
        clipped = gpd.clip(input_gdf, bbox_geom)

    else:
        raise ValueError("You must provide either 'clip_geom' or 'bbox'.")

    if output:
        clipped.to_file(output)

    return clipped


def add_unique_class(
    data: Union[str, "gpd.GeoDataFrame"],
    column: str,
    class_column: str = "class",
    mapping: Optional[Dict[str, int]] = None,
) -> "gpd.GeoDataFrame":
    """
    Add a unique integer class column to a vector dataset based on an existing column.

    Args:
        data (str or GeoDataFrame): Input vector data as file path or GeoDataFrame.
        column (str): The column name used for generating unique classes.
        class_column (str): The name of the new column to store integer classes. Default is "class".
        mapping (dict, optional): A dictionary mapping original values to integer classes.
            If not provided, a mapping will be generated automatically starting from 1.

    Returns:
        The updated GeoDataFrame with the new class column.
    """
    import geopandas as gpd

    gdf = gpd.read_file(data) if isinstance(data, str) else data.copy()

    if column not in gdf.columns:
        raise ValueError(f"Column '{column}' not found in the input data.")

    if mapping is None:
        unique_values = sorted(gdf[column].dropna().unique())
        mapping = {val: idx + 1 for idx, val in enumerate(unique_values)}

    gdf[class_column] = gdf[column].map(mapping)

    return gdf


def convert_to_cog(
    images: str,
    output_dir: str,
    prefix: str = "",
    suffix: str = "_cog",
    extra_options: Optional[List[str]] = None,
):
    """
    Convert all .tif files in a directory to Cloud Optimized GeoTIFFs (COGs).

    Args:
        images (str | list): Input directory containing .tif files, or a list of .tif file paths.
        output_dir (str): Path to the output directory where COGs will be saved.
        prefix (str): Prefix to add to the output filenames.
        suffix (str): Suffix to add to the output filenames before the .tif extension.
        extra_options (List[str], optional): Additional gdal_translate options.
            Example: ["-co", "TILED=YES", "-co", "BLOCKSIZE=512"]
    """
    import glob

    os.makedirs(output_dir, exist_ok=True)

    if isinstance(images, str):
        tif_files = glob.glob(os.path.join(images, "*.tif"))
    elif isinstance(images, list):
        tif_files = [tif for tif in images if tif.endswith(".tif")]
    else:
        raise ValueError("images must be a string or list of strings")

    if extra_options is None:
        extra_options = []

    for tif in tif_files:
        base = os.path.splitext(os.path.basename(tif))[0]
        out_file = os.path.join(output_dir, f"{prefix}{base}{suffix}.tif")

        cmd = ["gdal_translate", tif, out_file, "-of", "COG", "-co", "COMPRESS=DEFLATE"]
        cmd.extend(extra_options)

        print(f"Converting: {tif} -> {out_file}")
        subprocess.run(cmd, check=True)


def start_martin(
    db_url: Optional[str] = None,
    *,
    mbtiles: Optional[list[Union[str, Path]]] = None,
    pmtiles: Optional[list[Union[str, Path]]] = None,
    port: int = 3000,
    cache_dir: Optional[Union[str, Path]] = None,
    extra_args: Optional[list[str]] = None,
    pidfile: Optional[Union[str, Path]] = None,
    martin_version: str = "v0.20.0",
) -> subprocess.Popen:
    """
    Start Martin v0.20.0 serving from any combo of:
      - PostgreSQL (db_url)
      - MBTiles (mbtiles=[...])
      - PMTiles (pmtiles=[...])

    Args:
        db_url: The URL of the PostgreSQL database.
        mbtiles: A list of MBTiles files.
        pmtiles: A list of PMTiles files.
        port: The port to listen on.
        cache_dir: The directory to cache the Martin binary.
        extra_args: Extra arguments to pass to the Martin binary.
        pidfile: The file to write the PID to.
        martin_version: The version of Martin to use.

    Returns:
        A subprocess.Popen object representing the Martin process.
        Also writes a PID file for stop_martin().
    """

    import platform
    import stat
    from pathlib import Path

    BASE = (
        f"https://github.com/maplibre/martin/releases/download/martin-{martin_version}"
    )

    # Default cache + pidfile
    if cache_dir is None:
        cache_dir = Path("~/.cache/martin").expanduser()
    if pidfile is None:
        pidfile = cache_dir / f"martin-{martin_version}.pid"

    def _detect_asset_v0200() -> str:
        """Return the exact tar.gz asset name for v0.20.0 matching the current OS/arch."""
        sysname = platform.system().lower()
        machine = platform.machine().lower()

        if sysname.startswith("linux"):
            if machine in ("x86_64", "amd64"):
                # Prefer glibc build; fall back to musl if you need static
                return "martin-x86_64-unknown-linux-gnu.tar.gz"
            elif machine in ("aarch64", "arm64"):
                return "martin-aarch64-unknown-linux-musl.tar.gz"
            else:
                raise RuntimeError(f"Unsupported Linux arch: {machine}")

        if sysname.startswith("darwin"):
            if machine in ("x86_64", "amd64"):
                return "martin-x86_64-apple-darwin.tar.gz"
            elif machine in ("arm64", "aarch64"):
                return "martin-aarch64-apple-darwin.tar.gz"
            else:
                raise RuntimeError(f"Unsupported macOS arch: {machine}")

        raise RuntimeError("This helper supports Linux and macOS only.")

    def _ensure_exec(p: Path):
        p.chmod(p.stat().st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)

    if not any([db_url, mbtiles, pmtiles]):
        raise ValueError("Provide at least one source: db_url, mbtiles, or pmtiles.")
    cache_dir = Path(cache_dir).expanduser()
    pidfile = Path(pidfile)

    asset = _detect_asset_v0200()
    url = f"{BASE}/{asset}"
    tar_path = cache_dir / martin_version / asset
    bin_dir = cache_dir / martin_version

    print(f"Downloading Martin {martin_version} from {url} to {tar_path}")
    print(f"Downloading Martin {martin_version} to {bin_dir}")
    # 2) Extract (only once)
    martin_bin = bin_dir / "martin"
    if not martin_bin.exists():
        download_file(url, str(tar_path), quiet=True)

    _ensure_exec(martin_bin)

    # Build command (note: port set via --listen-addresses)
    args = [str(martin_bin), "--listen-addresses", f"0.0.0.0:{port}"]

    if extra_args:
        args.extend(extra_args)
    if db_url:
        args.append(db_url)
    if mbtiles:
        args.extend(str(p) for p in mbtiles)
    if pmtiles:
        args.extend(str(p) for p in pmtiles)

    # Launch
    proc = subprocess.Popen(args, env=os.environ.copy())
    pidfile.parent.mkdir(parents=True, exist_ok=True)
    pidfile.write_text(str(proc.pid), encoding="utf-8")
    print(f"Martin {martin_version} started (pid={proc.pid}) on port {port}")
    return proc


def stop_martin(
    proc: Optional[subprocess.Popen] = None,
    pidfile: Optional[Union[str, Path]] = None,
    timeout: float = 10.0,
    cache_dir: Optional[Union[str, Path]] = None,
    martin_version: str = "v0.18.1",
):
    """
    Stop Martin started via start_martin(). Use the Popen handle if you have it,
    otherwise it falls back to the PID file.

    Args:
        proc: The subprocess.Popen object representing the Martin process.
        pidfile: The file to read the PID from.
        timeout: The timeout in seconds to wait for the Martin process to terminate.
        cache_dir: The directory to cache the Martin binary.
        martin_version: The version of Martin to use.

    Returns:
        None

    Raises:
        subprocess.TimeoutExpired: If the Martin process does not terminate within the timeout.

    """
    import signal

    # Default cache + pidfile
    if cache_dir is None:
        cache_dir = Path("~/.cache/martin").expanduser()

    if pidfile is None:
        pidfile = cache_dir / f"martin-{martin_version}.pid"

    if proc is not None and proc.poll() is None:
        proc.terminate()
        try:
            proc.wait(timeout=timeout)
            print("Martin terminated via process handle.")
            return
        except subprocess.TimeoutExpired:
            proc.kill()
            print("Martin killed after timeout via process handle.")
            return

    pidfile = Path(pidfile)
    if not pidfile.exists():
        print("No PID file found; nothing to stop.")
        return

    try:
        pid = int(pidfile.read_text().strip())
    except Exception:
        print(f"PID file unreadable: {pidfile}")
        return

    try:
        os.kill(pid, signal.SIGTERM)
    except ProcessLookupError:
        print(f"No process with pid {pid}; removing stale PID file.")
        pidfile.unlink(missing_ok=True)
        return

    # wait briefly, then SIGKILL if needed
    import time

    t0 = time.time()
    while time.time() - t0 < timeout:
        try:
            os.kill(pid, 0)
        except ProcessLookupError:
            pidfile.unlink(missing_ok=True)
            print("Martin terminated.")
            return
        time.sleep(0.2)
    os.kill(pid, signal.SIGKILL)
    pidfile.unlink(missing_ok=True)
    print("Martin killed after timeout.")


def get_ee_tile_url(
    asset_id: str,
    vis_params: Optional[Union[str, dict]] = None,
    endpoint: Optional[str] = None,
) -> str:
    """
    Get the tile URL for an Earth Engine asset.

    Args:
        asset_id: The Earth Engine asset ID.
        vis_params: The visualization parameters.
        endpoint: The endpoint to use for the tile request. Set to "default" to use the default endpoint.

    Returns:
        The tile URL.

    Raises:
        ValueError: If the asset ID is not a valid Earth Engine asset ID.
        ValueError: If the visualization parameters are not a valid JSON string.
        ValueError: If the endpoint is not a valid URL.
        ValueError: If the data type is not supported.
    """

    if isinstance(endpoint, str):

        if endpoint == "default":
            endpoint = "https://giswqs-ee-tile-request.hf.space/tile"

        payload = {"asset_id": asset_id, "vis_params": vis_params or {}}

        response = requests.post(endpoint, json=payload)
        response.raise_for_status()
        return response.json()["tile_url"]

    try:

        import ee
        from geemap.ee_tile_layers import _get_tile_url_format, _validate_palette

        if isinstance(asset_id, str):
            if asset_id.startswith("ee."):
                ee_object = eval(asset_id)
            else:
                data_dict = ee.data.getAsset(asset_id)
                data_type = data_dict["type"]
                if data_type == "IMAGE":
                    ee_object = ee.Image(asset_id)
                elif data_type == "IMAGE_COLLECTION":
                    ee_object = ee.ImageCollection(asset_id)
                elif data_type in ["TABLE", "TABLE_COLLECTION"]:
                    ee_object = ee.FeatureCollection(asset_id)
                else:
                    raise ValueError(f"Unsupported data type: {data_type}")
        else:
            ee_object = asset_id

        if vis_params is None:
            vis_params = {}
        if isinstance(vis_params, str):
            if len(vis_params) == 0:
                vis_params = "{}"
            if vis_params.startswith("{") and vis_params.endswith("}"):
                vis_params = json.loads(vis_params)
            else:
                raise ValueError(f"Unsupported vis_params type: {type(vis_params)}")
        elif isinstance(vis_params, dict):
            pass
        else:
            raise ValueError(f"Unsupported vis_params type: {type(vis_params)}")

        if "palette" in vis_params:
            vis_params["palette"] = _validate_palette(vis_params["palette"])

        url = _get_tile_url_format(ee_object, vis_params)
        return url
    except Exception as e:
        return f"Error: {str(e)}"


def get_wayback_layers(url: str = None) -> dict:
    """
    Retrieve all layer dates and tile IDs from the ArcGIS Wayback WMTS capabilities URL.

    Args:
        url (str): WMTS capabilities URL. Default to
            https://wayback.maptiles.arcgis.com/arcgis/rest/services/world_imagery/mapserver/wmts/1.0.0/wmtscapabilities.xml

    Returns:
        dict: A dictionary where keys are layer dates (e.g., '2023-01-11')
              and values are corresponding tile IDs (e.g., '11475').
    """
    import requests
    import xml.etree.ElementTree as ET
    import re

    if url is None:
        url = "https://wayback.maptiles.arcgis.com/arcgis/rest/services/world_imagery/mapserver/wmts/1.0.0/wmtscapabilities.xml"

    response = requests.get(url)
    response.raise_for_status()

    root = ET.fromstring(response.content)
    ns = {
        "wmts": "https://www.opengis.net/wmts/1.0",
        "ows": "https://www.opengis.net/ows/1.1",
    }

    layers = {}
    for layer in root.findall(".//wmts:Layer", ns):
        title_elem = layer.find("ows:Title", ns)
        resource_elem = layer.find("wmts:ResourceURL", ns)

        if title_elem is not None and resource_elem is not None:
            title_text = title_elem.text
            tile_url = resource_elem.attrib.get("template", "")

            # Extract date from title using regex (YYYY-MM-DD)
            date_match = re.search(r"\d{4}-\d{2}-\d{2}", title_text)
            date = date_match.group(0) if date_match else None

            # Extract tile ID (sequence of digits after /tile/)
            tile_id = None
            if "/tile/" in tile_url:
                try:
                    tile_id = tile_url.split("/tile/")[1].split("/")[0]
                except IndexError:
                    pass

            if date and tile_id:
                layers[date] = tile_id

    return layers


def get_wayback_tile_url(
    date: str = None, layers: dict = None, quiet: bool = False
) -> str:
    """Generates a URL for Wayback Map Tiles.

    Args:
        date (str, optional): The date for which to retrieve the tile URL in 'YYYY-MM-DD' format.
                              If None, the latest available date is used.
        layers (dict, optional): A dictionary of available layers keyed by date.
                                 If None, the default layers are retrieved.
        quiet (bool, optional): If True, does not print any messages. Defaults to False.
    Returns:
        str: The formatted URL for the Wayback Map Tile corresponding to the specified date.

    Raises:
        ValueError: If the provided date is not available in the layers.

    Notes:
        If the specified date is not available, the latest date from the layers will be used.
    """
    if layers is None:
        layers = get_wayback_layers()
    dates = list(layers.keys())

    if date is None:
        date = dates[0]
    elif date not in dates:
        new_date = find_closest_date(date, dates)
        if not quiet:
            print(f"{date} is not available. Using the closest date: {new_date}")
        date = new_date

    tile_id = layers[date]
    tile_url = f"https://wayback.maptiles.arcgis.com/arcgis/rest/services/world_imagery/wmts/1.0.0/default028mm/mapserver/tile/{tile_id}/{{z}}/{{y}}/{{x}}"

    return tile_url


def get_wayback_tile_dict(layers: dict = None) -> dict:
    """Generates a dictionary of Wayback Map Tiles.

    Args:
        layers (dict, optional): A dictionary of layers. Defaults to None.

    Returns:
        dict: A dictionary of Wayback Map Tiles.
    """
    if layers is None:
        layers = get_wayback_layers()
    return {
        date: get_wayback_tile_url(date, layers)
        for date in reversed(list(layers.keys()))
    }


def find_closest_date(
    target_date: str, date_list: List[str], fmt: str = "%Y-%m-%d"
) -> Optional[str]:
    """Finds the closest date string in a list of date strings to a given target date.

    Args:
        target_date (str): The reference date string to compare against.
        date_list (List[str]): A list of date strings to search.
        fmt (str, optional): The date format used for parsing. Defaults to "%Y-%m-%d".

    Returns:
        Optional[str]: The date string from `date_list` that is closest to `target_date`,
            or None if `date_list` is empty or invalid.
    """
    from datetime import datetime
    from typing import List, Optional

    if not date_list:
        return None

    try:
        date_list = list(date_list)
    except Exception:
        print(f"date_list must be a list of date strings: {date_list}")
        return None

    if target_date is None:
        return date_list[0]

    try:
        target = datetime.strptime(target_date, fmt)
        parsed_dates = [(datetime.strptime(d, fmt), d) for d in date_list]
    except ValueError as e:
        print(f"Date parsing error: {e}")
        return None

    # Find the date with the smallest absolute time difference
    closest_date = min(parsed_dates, key=lambda x: abs(x[0] - target))[1]
    return closest_date


def get_raster_resolution(image_path: str) -> Tuple[float, float]:
    """Get pixel resolution from the raster using rasterio.

    Args:
        image_path: The path to the raster image.

    Returns:
        A tuple of (x resolution, y resolution).
    """
    import rasterio

    with rasterio.open(image_path) as src:
        res = src.res
    return res


def stack_bands(
    input_files: List[str],
    output_file: str,
    resolution: Optional[float] = None,
    dtype: Optional[str] = None,  # e.g., "UInt16", "Float32"
    temp_vrt: str = "stack.vrt",
    overwrite: bool = False,
    compress: str = "DEFLATE",
    output_format: str = "COG",
    extra_gdal_translate_args: Optional[List[str]] = None,
) -> str:
    """
    Stack bands from multiple images into a single multi-band GeoTIFF.

    Args:
        input_files (List[str]): List of input image paths.
        output_file (str): Path to the output stacked image.
        resolution (float, optional): Output resolution. If None, inferred from first image.
        dtype (str, optional): Output data type (e.g., "UInt16", "Float32").
        temp_vrt (str): Temporary VRT filename.
        overwrite (bool): Whether to overwrite the output file.
        compress (str): Compression method.
        output_format (str): GDAL output format (default is "COG").
        extra_gdal_translate_args (List[str], optional): Extra arguments for gdal_translate.

    Returns:
        str: Path to the output file.
    """

    if not input_files:
        raise ValueError("No input files provided.")
    elif isinstance(input_files, str):
        input_files = find_files(input_files, ".tif")

    if os.path.exists(output_file) and not overwrite:
        print(f"Output file already exists: {output_file}")
        return output_file

    # Infer resolution if not provided
    if resolution is None:
        resolution_x, resolution_y = get_raster_resolution(input_files[0])
    else:
        resolution_x = resolution_y = resolution

    # Step 1: Build VRT
    vrt_cmd = ["gdalbuildvrt", "-separate", temp_vrt] + input_files
    subprocess.run(vrt_cmd, check=True)

    # Step 2: Translate VRT to output GeoTIFF
    translate_cmd = [
        "gdal_translate",
        "-tr",
        str(resolution_x),
        str(resolution_y),
        temp_vrt,
        output_file,
        "-of",
        output_format,
        "-co",
        f"COMPRESS={compress}",
    ]

    if dtype:
        translate_cmd.insert(1, "-ot")
        translate_cmd.insert(2, dtype)

    if extra_gdal_translate_args:
        translate_cmd += extra_gdal_translate_args

    subprocess.run(translate_cmd, check=True)

    # Step 3: Clean up VRT
    if os.path.exists(temp_vrt):
        os.remove(temp_vrt)

    return output_file


def ee_initialize(
    key_data: Optional[str] = None, token_name: str = "EE_SERVICE_ACCOUNT"
):
    """
    Initialize the Earth Engine API.

    Args:
        key_data: The key data to use for authentication. Must be a JSON string containing service account credentials,
            with at least a 'client_email' field (e.g., the contents of a Google service account key file).
        token_name: The name of the environment variable to use for authentication.
    """
    import ee

    if key_data is None:
        key_data = get_api_key(token_name)
    if key_data is None:
        raise ValueError(
            f'No key data found in parameter or environment variable "{token_name}"'
        )

    try:
        email = json.loads(key_data)["client_email"]
    except json.JSONDecodeError as e:
        raise ValueError(f"Invalid JSON for key_data: {e}")
    except KeyError:
        raise ValueError("key_data JSON does not contain 'client_email'")
    credentials = ee.ServiceAccountCredentials(email=email, key_data=key_data)
    ee.Initialize(credentials)


def generate_latlon_grid(extent, dx=0.1, dy=0.1, crs="EPSG:4326", output=None):
    """
    Generate a rectangular lat/lon grid as polygons over a given extent.

    Parameters
    ----------
    extent : tuple
        (xmin, ymin, xmax, ymax) in degrees, e.g. (-180, -60, 180, 85)
    dx : float
        Longitude interval in degrees (cell width)
    dy : float
        Latitude interval in degrees (cell height)
    crs : str or dict
        Coordinate reference system for the output GeoDataFrame

    Returns
    -------
    GeoDataFrame
        Columns: id, lon_min, lat_min, lon_max, lat_max, geometry
    """

    import numpy as np
    import geopandas as gpd
    from shapely.geometry import Polygon

    xmin, ymin, xmax, ymax = extent

    # Ensure increasing order
    if xmax <= xmin or ymax <= ymin:
        raise ValueError("Invalid extent: xmax must be > xmin and ymax > ymin")

    # Generate grid coordinates (we’ll allow partial cells at the edges)
    lons = np.arange(xmin, xmax, dx)
    lats = np.arange(ymin, ymax, dy)

    polygons = []
    records = []

    cell_id = 0
    for lon in lons:
        for lat in lats:
            # Clip cells to the exact extent at the upper/right edges
            lon_max = min(lon + dx, xmax)
            lat_max = min(lat + dy, ymax)

            poly = Polygon(
                [
                    (lon, lat),
                    (lon_max, lat),
                    (lon_max, lat_max),
                    (lon, lat_max),
                    (lon, lat),
                ]
            )

            polygons.append(poly)
            records.append(
                {
                    "id": cell_id,
                    "lon_min": lon,
                    "lat_min": lat,
                    "lon_max": lon_max,
                    "lat_max": lat_max,
                }
            )
            cell_id += 1

    gdf = gpd.GeoDataFrame(records, geometry=polygons, crs=crs)

    if output is not None:
        gdf.to_file(output)

    return gdf
